Systems and methods for automated melting curve analysis

ABSTRACT

An experimental melting curve is modeled as a sum of a true melting curve and background fluorescence. A deviation function may be generated based upon the experimental melting curve data and a model of a background signal. The deviation function may be generated by segmenting a range of the experimental curve into a plurality of windows. Within each window, a fit between the model of the background signal and the experimental melting curve data may be calculated. The deviation function may be formed from the resulting fit parameters. The deviation function may include background signal compensation and, as such, may be used in various melting curve analysis operations, such as data visualization, clustering, genotyping, scanning, negative sample removal, and the like. The deviation function may be used to seed an automated background correction process. A background-corrected melting curve may be further processed to remove an aggregation signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of, and claims priority to U.S. patentapplication Ser. No. 13/132,856, entitled, “Systems and Methods forAutomated Melting Curve Analysis,” filed on Jun. 3, 2011, which is anational stage entry of PCT/US2010/034969, entitled, “Systems andMethods for Automated Melting Curve Analysis,” filed on May 14, 2010,which claims priority to U.S. Provisional Patent Application No.61/178,886 entitled, “Systems and Methods for Automated Melting CurveAnalysis,” filed on May 15, 2009, each of which is incorporated byreference in its entirety.

The sequence listing in the file named “sequenceListing_35328-101.txt”having a size of 3 KB that was created on Apr. 19, 2011 is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates to melting curve analysis and, in particular, tosystems and methods for automated analysis of the melting curve of acompound, such as a nucleic acid or protein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a plot depicting linear baseline melting curve analysis.

FIG. 2 is a plot of unmodified experimental melting curves of anunlabeled probe melting experiment, showing multiple genotypes andexponential background fluorescence at low temperatures.

FIG. 3 is a flow diagram of one embodiment of a method for generating adeviation function of an experimental melting curve.

FIG. 4 depicts plots of exemplary, unmodified experimental meltingcurves of hairpin structures.

FIG. 5 depicts derivative plots of the melting curves of FIG. 4.

FIG. 6 depicts derivative plots after exponential background subtractionof the melting curves of FIG. 4.

FIG. 7 depicts plots of deviation functions of the data presented inFIG. 4.

FIG. 8 depicts plots of normalized melting curves of the data presentedin FIG. 4, after exponential background subtraction.

FIG. 9 depicts plots of integrated deviation functions of the datapresented in FIG. 4.

FIG. 10 depicts derivative plots of melting curve data after exponentialbackground subtraction for multiplex genotyping.

FIG. 11 depicts deviation plots of the melting curve data presented inFIG. 10.

FIG. 12A depicts exemplary smoothed protein melting curves.

FIG. 12B depicts derivative plots of the smoothed protein melting curvesof FIG. 12A.

FIG. 12C depicts deviation function plots of the protein melting curvedata of FIG. 12A.

FIG. 12D depicts derivative melting curves after background correction.

FIG. 13 depicts derivative plots of normalized unfolding curves of theprotein melting curves of FIG. 12A.

FIG. 14A depicts exemplary smoothed protein melting curves.

FIG. 14B depicts derivative plots of the smoothed protein melting curvesof FIG. 14A.

FIG. 14C depicts deviation function plots of the protein melting curvedata of FIG. 14A.

FIG. 14D depicts derivative melting curves after background correction.

FIG. 15 depicts derivative plots of normalized unfolding curves of theprotein melting curves of FIG. 14A

FIG. 16 is a flow diagram of one embodiment of a method for identifyinga negative sample using deviation analysis.

FIG. 17 is a flow diagram of another embodiment of a method foridentifying a negative sample using deviation analysis.

FIG. 18 is a flow diagram of one embodiment of a method forautomatically identifying background and/or melting regions of a meltingcurve.

FIG. 19 depicts plots of exemplary deviation functions.

FIG. 20 is a flow diagram of one embodiment of a method forautomatically identifying amplicon and probe background and/or meltingregions.

FIG. 21A is a plot of a deviation function of a melting curve comprisingamplicon and probe melting regions.

FIG. 21B is a plot of a deviation function of a probe melting region.

FIG. 21C depicts another example of a process for identifying a cursorprobe region.

FIG. 22 is a flow diagram of one embodiment of a method for automatedbackground subtraction.

FIGS. 23A and 23B depict exemplary ideal and melting curves.

FIG. 24 is a flow diagram of another embodiment of a method forautomated background subtraction.

FIG. 25 depicts deviation plots that are correctly clusteredautomatically by unbiased hierarchal methods.

FIG. 26 depicts derivative plots after exponential background removalthat are not correctly clustered by unbiased hierarchal methods.

FIG. 27 depicts a set of unmodified melting curves after PCR meltinganalysis including negative samples.

FIG. 28 depicts a set of negative sample indicators after negativesample exclusion using an amplitude cut off technique.

FIG. 29 depicts a set of melting curves after negative sample exclusionusing deviation analysis.

FIG. 30 depicts a set of negative sample indicators after negativesample exclusion using deviation analysis.

FIG. 31 depicts deviation plots after the automatic location of a probemelting region and an amplicon melting region by deviation analysis.

FIG. 32 depicts a set of negative sample and cluster membershipindicators.

FIG. 33 is a block diagram of a system for analyzing melting curve data.

Additional aspects and advantages will be apparent from the followingdetailed description of preferred embodiments, which proceeds withreference to the accompanying drawings.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Melting curve analysis is useful in the study of various substances. Inparticular, nucleic acids have been studied extensively through meltingcurves, where differences in melting curves can be indicative ofdifferent nucleic acid sequences. Melting curves are also used in thestudy of protein binding, where characteristic melting curves areindicative of protein binding affinity for a particular ligand. Whilereference is made herein to nucleic acid and protein melting, it isunderstood that melting curve analysis of other compounds is within thescope of this disclosure.

In one example herein, melting curve analysis may provide informationregarding the identity and/or structure of a nucleic acid product. Theamount of energy required to break base-base hydrogen bonding withinnucleic acid structures (e.g., between two (2) strands of DNA) may bedependent upon factors relevant to the structure of the product. Thesefactors may include, but are not limited to length, complementarity,guanine-cytosine (GC) content, the presence or absence of repeatedsequences, and the like.

A melting curve may be obtained by applying a gradient of energy to(e.g., heating) a solution containing a nucleic acid product. As energyis added and the temperature of the solution increases, the product maydenature (e.g., disassociate). While the examples make reference toincrease in temperature, other methods of melting, e.g., a gradientchanging the ionic concentration, are known in the art. A melting curvemay be generated by measuring the extent to which this disassociationoccurs as a function of temperature (or other melting gradient). See,e.g., U.S. Pat. No. 5,871,908, herein incorporated by reference.Therefore, as used herein, a melting curve may refer to any datasetcomprising measurements quantifying the extent to which a compoundchanges its structure in response to a melting gradient, such astemperature or ionic concentration (e.g., the extent to which strands ina nucleic acid product disassociate as a function of the energy gradientapplied thereto).

In some embodiments, the disassociation may be measuredelectro-optically. The nucleic acid product (or other compound) may beplaced into a solution comprising a binding dye. The binding dye may beadapted to emit electro-optical (EO) radiation when bound to doublestranded DNA (dsDNA). As the product disassociates, the binding dye maycease emitting EO radiation (or, as discussed below, may emit EOradiation at a reduced level). Accordingly, a melting curve may begenerated by acquiring measurements of the EO radiation (fluorescence)emitted from the solution as energy is applied thereto (e.g., as thetemperature of the solution is increased). Moreover, it is understoodthat the disclosure is not limited to embodiments in which thefluorescence decreases during melting; in some embodiments, such asthose using G-quenching single labeled probes, the fluorescence signalmay increase upon melting (see, e.g., U.S. Pat. No. 6,635,427).

A melting curve may, therefore, comprise a series of EO radiationmeasurements (e.g., measurements of the fluorescence emitted from thesolution) as a function of temperature. However, the teachings of thisdisclosure may be applied to other melting curves comprisingdisassociation measurements acquired in other ways. Accordingly, thisdisclosure should not be read as limited to any particular method and/ortechnique for acquiring melting curve data (e.g., for acquiringmeasurements quantifying nucleic acid disassociation as a function ofthe energy applied to the solution).

As discussed above, information regarding the structure of a nucleicacid product may be inferred from a melting curve. As such, meltingcurve data may be used to examine polymerase chain reaction (PCR)products. A melting curve of a PCR product may be acquired by heating aproduct of a PCR reaction in the presence of a binding dye, which, asdiscussed above, may be adapted to fluoresce more strongly when bound todsDNA than when bound to single-stranded lengths of DNA (ssDNA).Therefore, at relatively low temperatures, where the PCR product mayexist primarily as dsDNA, the solution may fluoresce at a relativelyhigh level. As the temperature of the solution is increased, the productmay disassociate (e.g., denature) into two (2) strands of ssDNA, whichmay cause the solution to fluoresce at a lower level. Within a narrowtemperature window, the PCR product may undergo a phase transition froma dsDNA state to a ssDNA state. As described above, this transition mayreduce the fluorescence emitted by the solution. The temperature windowin which this transition occurs may be referred to as a melting region,a melting transition, and/or a melting window.

The binding dyes typically used in such melting curve experiments maynaturally fluoresce in solution as a function of temperature. Forexample, in the absence of dsDNA, the fluorescence signal of a bindingdye, such as LCGreen® Plus (which is available from and is a registeredtrademark of Idaho Technology, Inc.), may be monotonically decreasing asa function of temperature. Therefore, a melting curve acquired in themanner described above (e.g., by measuring the EO radiation emitted as asolution of nucleic acid product and binding dye is heated) may comprisea combination of the fluorescence emitted by dye bound to dsDNA productand background fluorescence produced naturally by the binding dye insolution and/or the dye bound to ssDNA.

Accordingly, the measured, raw fluorescence signal acquired by melting anucleic acid product in the presence of a binding dye may be modeled asa sum of fluorescence resulting from the product melt (disassociation ofthe product from a dsDNA to ssDNA as the solution is heated) andbackground fluorescence. Equation 1 shows an experimental melting curveF(T), comprising a sum of the “true” melting curve M(T) (e.g., thefluorescence produced by the product melt) and background fluorescenceB(T):

F(T)=M(T)+B(T)  Eq. 1

As discussed above, information regarding a nucleic acid product (e.g.,the product's structure, composition, and the like) may be inferredand/or determined from an experimental melting curve F(T). However,analysis of the experimental melting curve data F(T) may be complicatedby the background fluorescence B(T) component thereof. Various systemsand methods have been developed to model and remove the backgroundfluorescence B(T) signal from experimental melting curve data F(T).

In one example, the background fluorescence B(T) is modeled as a linearfunction. The fluorescence of many common dyes decreases linearly withtemperature (decreases with increasing temperature over certaintemperature ranges). In a nucleic acid melting curve, the fluorescenceof the product drops rapidly within the melting region. However, outsideof the melting region, the fluorescence variation with temperature isapproximately linear. Therefore, an experimental melting curve may benormalized by extrapolating linear baselines before and after themelting transitions.

FIG. 1 depicts an experimental melting curve F(T) having linearbaselines L₁(T) and L₀(T). A normalized melting curve may be calculatedfrom the height of the experimental melting curve F(T) above the lowerbaseline, L₀(T) as a proportion of the difference between upper andlower baselines, which may or may not have the same slope.

FIG. 2 depicts plots of melting curves obtained by melting the productof an asymmetric PCR reaction in the presence of an unlabeled probe. Thecurve includes both unlabeled probe and PCR product melting transitions.As illustrated in FIG. 2, the use of the linear baseline methoddescribed above is problematic because the upper and lower linearbaselines intersect below the melting curve due to its non-linearity,and the denominator of the linear compensation equation depicted in FIG.1 goes to zero (0).

In an alternative approach, background fluorescence B(T) may be modeledusing an exponential decay function. Systems and methods for exponentialbackground modeling and subtraction are provided in PCT Application No.WO2007/035806, filed on Sep. 20, 2006, and entitled, “MELTING CURVEANALYSIS WITH EXPONENTIAL BACKGROUND SUBTRACTION,” which is herebyincorporated by reference in its entirety.

Empirical evidence suggests that, at certain temperatures (e.g.,temperatures less than 85° C.), the background fluorescence signal froma binding dye may be accurately modeled as a decaying exponential of thefollowing form:

B(T)=Ce ^(a(T−T) ^(L) ⁾  Eq. 2

In Equation 2, C and a are constants to be fit from the melting curvedata F(T), and T_(L) is a shifting parameter for the argument of theexponent (a cursor location, discussed below), which is typicallylocated below a melting transition within the melting curve F(T).

Due to, inter alia, a change in scale of the background fluorescencebefore and after the melting region, the model of the backgroundfluorescence of Equation 2 may not directly fit observed fluorescencedata. Based on the properties of the exponential used to model thebackground fluorescence B(T) (e.g., that the derivative of anexponential is itself an exponential), Equation 3 may be obtained:

B′=aCe ^(a(T−T) ^(L) ⁾  Eq. 3

According to the background fluorescence model, before and after themelt transition region, the product melting function is constant. Twotemperatures may be selected to bracket the melting transition region: afirst temperature T_(L) may be selected before the melt transitionregion, and a second temperature T_(R) may be selected after the melttransition region. These temperatures may be referred to herein as“normalization cursors.” The normalization cursors T_(L) and T_(R) maybe used to construct a model of an exponential background signal B(T) bycombining Equations 2 and 3:

F′(T _(L))=B′(T _(L))=aC  Eq. 4a

F′(T _(R))=B′(T _(R))=aCe ^(a(T) ^(R) ^(−T) ^(L) ⁾  Eq. 4b

The derivative of the observed fluorescence data may be approximatedusing, e.g., a central differencing technique. Equations 4a and 4b maybe solved for a and C, yielding Equations 5a and 5b:

$\begin{matrix}{a = \frac{\ln \left( {{B^{\prime}\left( T_{R} \right)}/{B^{\prime}\left( T_{L} \right)}} \right)}{T_{R} - T_{L}}} & {{{Eq}.\mspace{11mu} 5}a} \\{C = \frac{B^{\prime}\left( T_{L} \right)}{a}} & {{{Eq}.\mspace{14mu} 5}b}\end{matrix}$

The results of Equations 5a and 5b may be used to construct a model ofthe background fluorescence B(T). The model may be subtracted from theexperimental melting curve F(T), resulting in a “true” melting curveM(T):

M(T)=F(T)−Ce ^(a(T−T) ^(L) ⁾  Eq. 6

Typically, a human operator manually selects the normalization cursorlocations (T_(L) and T_(R)) used to model the background fluorescenceB(T) (e.g., in Equations 3-6). This operation may require that theoperator have some prior knowledge of the melting curve data and/or havethe skills and experience to properly interpret raw, experimentalmelting curve data F(T) (e.g., know where the melting transition occurs,etc.). The systems and methods disclosed herein may provide forautomated analysis of an experimental melting curve F(T) by identifyingbackground and melting regions within melting curve data F(T) usingdeviation analysis (described below). Accordingly, the systems andmethods for deviation analysis disclosed herein may obviate the need forthis manual operation (e.g., remove the need for prior knowledge and/ormanual estimation of the melting regions).

In some applications, melting curve data may be displayed as anormalized fluorescence curve, in which the melting curve M(T) isre-scaled, illustratively from one (1) (completely annealed) to zero (0)(completely disassociated). In some embodiments, a melting curve M(T)may be normalized to N(T) using the following transformation:

$\begin{matrix}{{N(T)} = \frac{{M(T)} - {\min \left\{ {M(T)} \right\}}}{{\max \left\{ {M(T)} \right\}} - {\min \left\{ {M(T)} \right\}}}} & {{Eq}.\mspace{11mu} 7}\end{matrix}$

Linear and exponential background modeling and removal techniques may beuseful in many applications. For example, linear models are a good fitfor PCR products with Tms between 80 and 95° C., and exponential modelsare a good fit when the temperature range analyzed is 20° C. or less.However, at temperatures <80° C. and/or when the temperature rangeanalyzed is >20° C., the background fluorescence B(T) signal (comprisingthe fluorescence produced by buffers, dNTPs, primers, etc.) may notconform to either linear or exponential models. Specifically,temperatures less than 80° C. show deviation from expected backgrounds,while temperatures less than 70° C. show even greater deviation, withprogressively increasing deviation at 60° C., 50° C., and 40° C. Interms of temperature ranges, at ranges >20° C., background may alsodeviate from simple linear and exponential models. As the analyzed rangeincreases through 30° C., 40° C., 50° C., and 60° C., the deviation ofbackground from linear or exponential models increases. Under theseconditions of low temperature and/or extended range analysis, deviationanalysis as described below may be a good alternative to fixed modeling.For example, unlabeled probes, snapback primers, and multiplex smallamplicon melting all can result in multiple transitions, often coveringa range of >20° C. with some melting transitions occurring below 80° C.Further information on unlabeled probes, snapback primers, and multiplexsmall amplicon melting can be found in L. Zhou et al., Snapback PrimerGenotyping with Saturating DNA Dye and Melting Analysis, 54(10) Clin.Chem. 1648-56 (October 2008), U.S. Pat. No. 7,387,887, and PCTPublication No. WO2008/109823, filed Mar. 7, 2008, and entitled,“PRIMERS FOR MELTING ANALYSIS,” which are hereby incorporated byreference in their entirety.

While nucleic acid melting curves have been used to provide informationregarding the sequence of nucleic acids, protein melting curves areoften used to measure protein thermodynamic stability. To assess proteinthermal stability, the temperature is increased to above that in whichthe protein's native structure is thermodynamically stable, and theprotein unfolds, exposing hydrophobic amino acid residues that werepreviously sequestered in the protein structure. With protein melting,Tm is often defined as a midpoint in the thermal ramp and represents atemperature where the free energy of the native and nonnative forms areequivalent. Illustratively, the protein is melted both independently andin the presence of a ligand, and stability perturbations can be used toscreen libraries. Further information may be found in D. Matulis et al.,Thermodynamic Stability of Carbonic Anhydrase: Measurements of BindingAffinity and Stoichiometry Using ThermoFluor, Biochemistry 2005, 44,5258-5266, hereby incorporated by reference in its entirety.

Similar to the nucleic acid melting curve analysis discussed above,protein melting curves can be expressed in the form of Equation 1,F(T)=M(T)+B(T). A main difference between the typical protein melt andthe typical nucleic acid melt, B(T), is that the EO radiation signalincreases as the protein denatures. Typical protein melting curves areshown in FIGS. 12A and 14A. In the FIG. 12A example, as the temperatureincreases toward 40 degrees, thermal quenching causes a decrease in thefluorescence. Depending on the protein, unfolding usually starts between40 and 60 C and is observed as increasing fluorescence as more dye bindsto the exposed hydrophobic residues. Finally, fluorescence againdecreases as protein aggregation and precipitation occurs (usuallybetween 45 and 75 C) combined with additional thermal quenching. Themelting of six well known proteins are shown in FIG. 12A, all at aconcentration of 2.25 uM, demonstrating the range of stabilities andintensities typically observed.

Empirical evidence suggests that, at high temperatures above the meltingtransition (e.g., 60-90° C. depending on the protein), the residualfluorescence from a binding dye may be modeled by a quadratic polynomialof the following form:

B(T)=a _(i) T ² +b _(i) T+c _(i)  Eq. 8

In Equation 8, a_(i), b_(i), and c_(i) are constants to be fit from theexperimental melting curve data F(T), The protein melting curve then canbe expressed in the form F(T)=M(T)+B(T) as described above. SeeEquation 1. Typically, the constants in Equation 8 are found by aleast-squares fit to the collected fluorescence data over a continuoustemperature range in the background region.

Although the disclosure teaches the use of exemplary exponential andquadratic models of the background fluorescence signal, the disclosureis not limited in this regard. As would be understood by one of skill inthe art, the deviation function (and related analysis techniques) taughtherein could be adapted to operate with any modeling technique and/orform known in the art.

As noted above, typically, a human operator manually selects thetemperature region used to model the background fluorescence B(T) in amelting curve (e.g., in Equation 8). This operation may require that theoperator have some prior knowledge of the melting curve data and/or havethe skills and experience to properly interpret raw, experimentalmelting curve data F(T) (e.g., know where the melting transition occurs,etc.). The systems and methods disclosed herein may provide forautomated analysis of an experimental melting curve F(T) by identifyingbackground and melting regions within melting curve data F(T) usingdeviation analysis (described below). Accordingly, the systems andmethods for deviation analysis disclosed herein may obviate the need forthis manual operation (e.g., remove the need for prior knowledge and/ormanual estimation of the melting regions and background regions).

The melting curve data may be displayed in a derivative form (e.g., as aderivative or negative derivative of the normalized melting curve N(T)).However, since the melting curve M(T) may be collected at discretetemperature measurements, not always equally spaced, and may includesmall amounts of noise, the data may be smoothed (e.g., using acubic-smoothing spline) and/or resampled at uniform temperaturemeasurements. A derivative of the melting curve may be approximatedusing central differencing or another technique. For a melting curvecomprising a single melting transition, the peak of the derivative curvemay be denoted as a “melting transition” or T_(M). For melting curvescomprising multiple melting transitions, melting transition peaks may beidentified and/or numbered accordingly (e.g., as T_(M1), T_(M2), . . . ,T_(Mn)).

Melting curve data may be displayed and/or analyzed in terms of adeviation function, which may quantify the extent to which experimentalmelting curve data F(T) deviates from a model of background fluorescenceB(T). As discussed above, in some embodiments, the backgroundfluorescence B(T) may be modeled using, inter alia, an exponential decayfunction (e.g., an “ideal” modeling of a melting curve). Therefore, adeviation function may be based upon a deviation between an exponentialdecay rate of the experimental melting curve F(T) and that of thebackground fluorescence model (e.g., according to Equation 2 above).

Deviation analysis may comprise generating a plurality of fit parameterscalculated by fitting an experimental melting curve F(T) to apre-defined function within a series of temperature windows. Therefore,the deviation function (referred to herein as E(T)) may quantify theextent to which the experimental melting curve F(T) deviates from thepre-defined function as a function of temperature.

As will be discussed below, the deviation function E(T) of a meltingcurve may be used to analyze melting curve data directly (e.g., byinspection, visualization, plotting, etc.) and/or may be used withinother melting curve analysis processes or systems. Applications of thedeviation function E(T) disclosed herein include, but are not limitedto, displaying or plotting melting curve data (e.g., to highlightdifferences between melting curves for use in genotyping, scanning, andthe like), automatically identifying negative samples (e.g., negativecontrol samples, invalid data, etc.), automating melting region and/orbackground region identification, automating melting curve clustering,automating genotyping and/or scanning operations, automating backgroundfluorescence B(T) subtraction, and the like. One of skill in the art,however, would recognize that the systems and method for deviationanalysis disclosed herein could be used in other melting curveapplications. Therefore, the systems and methods for generating and/orapplying deviation analysis to melting curve analysis disclosed hereinshould not be read as limited to any particular set of applications.

Generating a deviation function E(T) may comprise calculating a runningfit between an experimental melting curve F(T) and a pre-determinedfunction. The pre-determined function may comprise a model of backgroundfluorescence B(T) within the experimental melting curve F(T) (referredto herein as an ideal melting curve), which, as discussed above, may beapproximated using an exponential decay function. See Equations 2-6.

The experimental melting curve F(T) may be defined within thetemperature interval [T_(min),T_(max)]. The running fit may be performedwithin a plurality of temperature windows (T_(W)) each having a width Wwithin the temperature interval of the experimental melting curve (e.g.,[T_(min), T_(max)] or [T_(min), T_(max)−W]):

T _(W) ≡T ε[T _(WL) ,T _(WL) +W]  Eq. 9

In Equation 9, T_(WL) may represent a minimum or “start” temperature ofthe temperature window T_(W). The width W of the temperature windowsT_(W) may be selected according to the resolution of the melting curvedata (e.g., the density and/or precision of the experimental meltingcurve F(T) data) and/or the features to be extracted from the meltingcurve data. The width W may be selected to be large enough to smooth outrandom noise variations within the temperature windows T_(W), whileremaining small enough to resolve features of interest.

The deviation function E(T) is defined on a uniform discretization ofthe interval [T_(min)+W/2, T_(max)−W/2], denoted by T. For example, atemperature interval ΔT between temperature windows T_(W) may be definedas:

T ₁ =T _(min) +W/2,T ₂ =T ₁ +ΔT, . . . , T _(n) =T _(max) −W/2  Eq. 10

The selection of ΔT (the spacing between temperature windows T_(W)) maybe based on the resolution of the experimental melting curve F(T),performance considerations, the nature of the features to be extractedtherefrom, or the like. The temperature interval ΔT may be selected tobe greater than a maximum difference between any two successive meltingcurve data points (e.g., greater than the coarsest temperatureresolution within the experimental melting curve F(T)).

Within each temperature window T_(W), a fit between the pre-definedfunction and the experimental melting curve F(T) may be calculated. Eachfit may result in a fit parameter, which may be assigned to a point(temperature value) associated with the temperature window T_(W). Thetemperature point associated with a particular temperature window T_(W)may be referred to as T, (for the temperature window comprising therange [T_(i)−W/2, T_(i)+W/2]).

Illustratively, this form of the deviation function is suitable fornucleic acid melt curves. As discussed above, the pre-defined functionused to generate the deviation function E(T) may be an exponential decayfunction configured to model background fluorescence B(T) (e.g., an“ideal” melting curve) in the experimental melting curve. Where thepre-defined function comprises an exponential decay function, the fitmay comprise selecting parameter(s) C_(i) and/or a_(i), such that:

$\begin{matrix}{{C_{i}^{a_{i}({T - T_{i} - \frac{W}{2}})}} \approx {F(T)}} & {{Eq}.\mspace{11mu} 11}\end{matrix}$

The fit of Equation 11 may be made using any fitting technique known inthe art, such as, for example, a least squares fitting technique.

In some embodiments, the exponential form of Equation 11 may be shiftedto the leftmost temperature value (e.g., left-shifted) within thetemperature window T_(W) for numerical stability.

The exponential decay factor a_(i) may be used to form the deviationfunction E(T), such that, for each fit parameter T_(i):

E(T _(i))=a _(i)  Eq. 12

As shown above, the deviation function of Equation 12 quantifies thedeviation between the exponential decay factor of the experimentalmelting curve F(T) and the “ideal” melting curve as a function oftemperature. For pure exponential background, the exponential decayfactor may be constant. Therefore, any deviation from that constant maybe a result of duplex melting (e.g., melting of a nucleic acid productin combination with the background decay). In some embodiments, in orderto display the deviation from the exponential, the minimum value of thedeviation function may be subtracted therefrom. Multiple curves may benormalized to each other by peak height (e.g.,E(T)−min{E(T)}/max{E(T)}−min{E(T)}). Alternatively, or in addition,normalization by total peak area may be performed by dividing each curveby numerical integration. Peak area normalization may be advantageousbecause integrated deviation plots E(T) (analogous to normalized meltingcurves) may all begin and end at the same values.

Alternatively, or in addition, the amplitude constant C_(i) and/or acombination of the amplitude and decay factors C_(i) and/or a_(i) may beincorporated into the deviation function E(T). In other embodiments, adeviation function may quantify a deviation between melting curve dataand another model of background fluorescence B(T) (e.g., a quadraticmodel, a discrete model, or the like). Therefore, the deviation functiondisclosed herein should not be read as limited to any particularpre-determined fit function.

For protein melting curves, the pre-defined function used to generatethe deviation function E(T) illustratively may be a quadratic polynomialto model background fluorescence B(T) in the experimental melting curvedata. Where the pre-defined function comprises a quadratic polynomial,the fit may comprise selecting parameter(s) a_(i), b_(i) and/or c_(i),such that:

a _(i) T ² +b _(i) T+c _(i) ≈F(T)  Eq. 13

The fit of Equation 13 may be made using any fitting technique known inthe art, such as, for example, a least squares fitting technique.

The constant multiplying the quadratic term a_(i) may be used to formthe deviation function E(T), such that, for each fit parameter T_(i):

E(T _(i))=a _(i)  Eq. 14

As shown above, the deviation function of Equation 14 quantifies thedeviation between the experimental melting curve F(T) and the “ideal”melting curve as a function of temperature. For pure quadraticbackground, the amplitude factor may be constant. Therefore, anydeviation from that constant may be a result of protein unfolding.

As will be discussed below, the deviation function E(T) may be used inthe analysis of experimental melting curve data F(T) (e.g., for use inmelting/background region identification, background fluorescenceremoval, negative sample identification, clustering, and so on). Sincethe deviation function E(T) may quantify a deviation between a model ofbackground fluorescence B(T) and the experimental melting curve F(T),the deviation function E(T) may inherently include backgroundfluorescence B(T) compensation, which (in some cases) may obviate theneed for dedicated background subtraction processing (e.g., using linearand/or exponential background subtraction).

FIG. 3 depicts one embodiment of a method 300 for generating a deviationfunction E(T) of an experimental melting curve F(T). At step 310, themethod 300 may be initialized, which may comprise allocating and/orinitializing resources required by the method 300. In some embodiments,the method 300 may be embodied as instructions and/or discrete softwaremodules stored on a computer-readable storage medium. Therefore, theinitialization of step 310 may include a computing device reading and/orloading the instructions into a memory or other device. Alternatively,or in addition, the method 300 may include one or more hardwarecomponents, such as one or more processors, sensors, Field ProgrammableGate Arrays, Application Specific Integrated Circuits, digital logic,and the like.

At step 320, the method 300 accesses melting curve data, which mayinclude an experimental melting curve F(T).

At step 330, the temperature range of the experimental melting curveF(T) may be tiled by a plurality of temperature windows T. Eachtemperature window T_(W) may be defined to have a width W. The width Wmay be selected by the method 300 (or a user thereof) according to theresolution of the experimental melting curve F(T) and/or the nature ofthe features to be extracted from the melting curve data. As discussedabove, the temperature windows T_(W) may be defined to form a uniformdiscretization of a temperature interval of the experimental meltingcurve F(T) and may overlap one another according to a ΔT metric, whichmay define the spacing between temperature windows T_(W).

At step 340, the method 300 may iterate over each of the plurality oftemperature windows T_(W). Accordingly, at step 340, the method 300 maydetermine whether there are additional temperature windows T_(W) toprocess and, if so, the flow may continue to step 342, where a deviationparameter for a next temperature window T_(W) may be calculated;otherwise, the flow may continue to step 350.

At step 342, a fit between the experimental melting curve F(T) and amodel of the ideal background fluorescence (within a current temperaturewindow T_(W)) may be calculated. As discussed above, in someembodiments, the background fluorescence may be modeled as anexponential decay function. One example of a fit between an experimentalmelting curve F(T) (e.g., a nucleic acid melting curve) and anexponential decay function is provided above in conjunction withEquations 11-12. In other embodiments, the background fluorescence maybe modeled using a quadratic function (or other model). An example of afit between an experimental melting curve (e.g., a protein meltingcurve) and a quadratic model is provided above in conjunction withEquations 13-14. Step 342 may further comprise determining a fitparameter for the temperature window T_(W). As discussed above, thefitting parameters and/or windows may be left-shifted for numericalstability.

At step 350, the fit parameter of each temperature window T_(W) may beused to generate a deviation function E(T). In some embodiments, step350 may further comprise normalizing the deviation function E(T).

At step 360, the deviation function E(T) may be made available fordisplay, further melting curve analysis, and the like. Step 360 maycomprise storing a representation of the deviation function E(T) on acomputer-readable media, making the representation available to one ormore users, displaying the representation on a human machine interface(HMI) (e.g., a display, a printer, etc.), or the like. Step 360 mayfurther comprise transmitting and/or making available the deviationfunction E(T) to one or more other processes and/or systems. Forexample, as will be discussed below, the deviation function E(T) may beused in an automated negative sample identification process, anautomated background subtraction process, or the like.

The deviation function E(T) generated according to method 300 describedabove may be used to display and/or analyze experimental melting curvedata F(T). In one example, the following oligonucleotides weresynthesized using standard methods:

S5D:  (Seq. ID No. 1) gttaaccACTGAtagcacgacgTCAGT  S7D:  (Seq. ID No. 2)gttaaccACTGACAtagcacgacgTGTCAGT  S9D:  (Seq. ID No. 3)gttaaccACTGACAGTtagcacgacgACTGTCAGT 

The capitalized regions of each of the above oligonucleotides arecomplementary so that they form intramolecular hairpins with stemregions of five (5), seven (7), or nine (9) base pairs (bp) at lowtemperatures. For each hairpin, a ten (10) base loop is present. Theshort end of the each hairpin will be extended by seven (7) bases in thepresence of a polymerase, forming stem regions of twelve (12), fourteen(14), or sixteen (16) bases, respectively.

Melting curve data were generated by preparing a solution comprising theoligonucleotides disclosed above. The solution included one (1) μM ofeach oligonucleotide in a PCR buffer (e.g., comprising 50 mM Tris, pH8.3, 3 mM MgCl₂, 500 μg/ml bovine serum albumin), 200 μM each dNTP, anda dye (e.g., 1×LCGreen® Plus dye available from Idaho Technology, Inc.)in a final volume of 10 μl. In some reactions, the solution included 0.5U KlenTaq 1 (AB Peptides), resulting in hairpins of 12, 14, and 16basepairs upon extension. Melting curve data were obtained usingLightCycler® capillary tubes (which is available from and is aregistered trademark of Roche Diagnostics, GmbH), in an HR-1™ highresolution melting instrument (available from Idaho Technology, Inc.) at0.3° C./s.

FIG. 4 shows examples of unmodified melting curves obtained using theHR-1™ instrument (available from Idaho Technology, Inc.). The HR-1™ maybe configured to adjust the gain automatically so that each meltingcurve begins at a fluorescence value of 90. The exponential character ofthe curves is apparent, and some melting behavior is suggested at about75° C. for the longer hairpins. However, it is not easy to interpret theunmodified curves displayed in FIG. 4, and it is not clear whether thereis any observable duplex melting for the shorter hairpins.

FIG. 5 is a derivative plot of the same data shown in FIG. 4. The highertemperature duplex transitions are apparent as peaks. However, it may bedifficult to identify the Tms of the lower temperature transitionsbecause of the rising background at low temperatures. Therefore, withoutfurther manipulation and/or processing (e.g., background removal),derivative plots may not be capable of adequately representing and/oradjusting for the background fluorescence B(T) in the curves.

As discussed above (e.g., in conjunction with Equations 1-6), thebackground fluorescence B(T) component of an experimental melting curvemay be subtracted from the melting curve F(T) to thereby yield anapproximation of the “true” melting curve M(T). See Equations 1-6 above.FIGS. 18 and 24 (discussed below) provide examples of automatedbackground removal processes using inter alia deviation analysis.

FIG. 6 depicts a derivative plot of the same data as FIGS. 4 and 5 afterexponential background subtraction. In the FIG. 6 plot, although allsamples show a melting transition, the performance of the backgroundsubtraction is not ideal, particularly for the 5 by hairpin duplex.

A deviation function E(T) of each of the experimental melting curves ofFIGS. 3-6 may be generated (e.g., using Equations 9-14 and/or method 300of FIG. 3). FIG. 7 is a plot of deviation functions E(T) normalized byarea so that numerical integration varies between one (1) and zero (0).As shown in FIG. 7, the deviation plots are denoted with E_(m)(T),which, as discussed below, is a function derived from the deviationfunction E(T) such that, within each temperature window, a minimal valueof the deviation function E(T) is subtracted therefrom. See Equation 20below. As illustrated in FIG. 7, the use of the deviation function E(T)(E_(M) (T) in the FIG. 7 example) results in relevant features withinthe melting curve data to be more pronounced and readily observable. Forinstance, FIG. 7 shows that the hairpin Tms are clearly spread over a30° C. range from 47-77° C. As expected, the shorter stems melt over abroader range than longer stems, and all transitions are displayed.Since the deviation function E(T) (or E_(M)(T) as depicted in FIG. 7)inherently adjusts for background fluorescence B(T), the background isremoved appropriately on all samples.

Instead of derivative plots, normalized melting curves can be displayedafter exponential background subtraction. The hairpin data analyzed inthis way are shown in FIG. 8. Although the melting curves at highertemperatures appear adequate, greater deviations from expected areobserved at lower temperatures, and the five (5) base pair duplexdisplays a “physically impossible” increase in fluorescence withtemperature in some ranges.

FIG. 9 shows an integrated deviation plot (shown as a percentage ofcumulative deviation) of the same data depicted in FIGS. 6-8. In thiscase, all curves appear reasonable with the longer duplexes showingsharper transitions as expected. Whether displayed asderivative/deviation plots or their melting curve/integrated forms,plots generated using a deviation function E(T) may be more robust,allowing for the comparison of multiple curves that cover a largetemperature range.

In another example, multiplex genotyping of at least four (4) singlebase variants with two temperature control calibrators is performedhomogeneously without probes. The oligonucleotide sequences formultiplex primers and the internal controls have been previouslypublished by Seipp M T et al., Quadruplex Genotyping of F5, F2, andMTHFR Variants in a Single Closed Tube by High-Resolution AmpliconMelting, 54(a) Clin. Chem. 108-15 (January 2008), which is herebyincorporated by reference in its entirety.

In this example, the following 50 bp low temperature control was used:

(Seq. ID No. 4) ATCGTGATTTCTATAGTTATCTAAGTAGTTGGCATTAATAATTTCATTTT

The complement of the above may be mixed with the above in equal molarproportions as determined by absorbance at 260 nm. Temperature controloligonucleotides may be blocked with a 3′-phosphate. The following 50 bphigh temperature control was used:

(Seq. ID No. 5) (G)CGGTC(A)GTCGG(C)CTAGCGGT(A)GCCAG(C)TGCGGC(A)CTGCGTG(A)CGCTCA(G) 

The control may further comprise the complement, where the bold bases inparenthesis are locked nucleic acids (LNAs) on the listed strand only.

A PCR amplification was performed in 10 μl volumes with 1× LightCycler®FastStart DNA Master HybProbes (available from Roche Diagnostics, Gmbh),0.5 μM each of the FV primers, 0.15 μM each of the MTHFR 1298 and 677primers, 0.16 μM each of the F2 primers, 0.06 μM of the low temperaturecorrection control and 0.08 μM of the high temperature correctioncontrol, 3.5 mM MgCl₂ (including 1 mM MgCl₂ contributed by theLightCycler® Master solution), 0.01 U/μl heat-labile uracil-DNAglycosylase (available from Roche Diagnostics, GmbH) 1× LCGreen® Plus(available from Idaho Technology, Inc.), and 20 ng of template DNA.

In the example, the PCR and a high resolution melting experiment wereperformed using an LS32™ device (available from Idaho Technology, Inc.).The PCR was performed using an initial hold of 95° C. for 10 min,followed by fifteen (15) cycles of 95° C. for 2 seconds, 56° C. for 1 s,and 72° C. for 1 s, and 25 cycles of 95° C. for 2 seconds, 58° C. for 1second, and 72° C. for 4 seconds. During amplification, no fluorescenceacquisition was performed to avoid prolonging the temperature cycles.All heating and cooling steps during PCR were done with ramp ratesprogrammed at 20° C./s. After PCR, samples were cooled (10° C./s) from95° C. to 40° C. and melting curves generated with continuousfluorescence acquisition from 55° C. to 95° C. at 0.3° C./second.

The melting curve data so obtained were processed to remove exponentialbackground fluorescence B(T) and normalized as described above. FIG. 10depicts a plot of a derivative of the processed melting curves. FIG. 10shows melting temperatures spanning a 25° C. range with the lowtemperature control peak at around 68° C. However, even after increasingthe amount of high temperature control (small right peak at 92-93° C.),it is apparent that intensity is low, making temperature adjustmentusing the high temperature control peak difficult.

The apparent relative intensity of higher temperature peaks may beincreased by applying the deviation analysis techniques described above(e.g., in Equations 9-14 and/or method 300 of FIG. 3). In this example,respective deviation functions E(T) were generated using the meltingcurve data.

Plots of these deviation functions E(T) (in the E_(m)(T) form discussedbelow in conjunction with Equation 20) are provided in FIG. 11. As shownin FIG. 11, the deviation analysis increases the apparent magnitude ofhigh temperature transitions relative to low temperature transitions.Correct genotyping of the four (4) central peaks was obtained by bothmethods of analysis.

FIG. 12A shows experimental melting curves of six different proteins:purified lysozyme; C reactive protein; IgG; citrate synthase; malicdehydrogenase; and alkaline phosphatase (all from Sigma-Aldrich®). Theseproteins were each dialyzed separately against isotonic phosphate buffersaline, pH 7.4 (PBS) and diluted in PBS to a protein concentration of2.25 uM in the presence of 5×SYPRO® Orange (available from Invitrogen®).Ten ul reactions were melted at 1° C./min in a LightScanner® (availablefrom Idaho Technology) between 35 and 99° C. The experimental meltingcurve data was smoothed using a cubic-smoothing spline and resampled atuniform temperature measurements.

FIG. 12B depicts derivative plots of the experimental protein meltingcurves of FIG. 12A. The derivatives may be approximated using a centraldifferencing technique.

FIG. 12C depicts deviation function E(T) plots of the experimentalprotein melting curve data of FIG. 12A. The deviation functions of FIG.12C may be calculated using Equations 9-14 and/or method 300 discussedabove. In the FIG. 12C example, the background fluorescence signal wasmodeled using the quadratic polynomial of Equation 13, and the windowwidth was twenty data points. The deviation function was formed from theconstants multiplying the quadratic term of each fit (a_(i) in Equation13).

FIG. 12D depicts derivative melting curves after background correction.For the background removal, cursor locations are manually set at 84.4degrees and 98.4 degrees (both located in the background region abovethe melting features for all samples). For each sample, a quadraticpolynomial is fit to the smooth melting curve data (depicted in FIG.12A), in a least-squares sense, within the cursor locations as describedabove. A background-corrected melting curve is formed by subtracting thebackground model from the smoothed experimental melting curve data. Thebackground corrected melting curves were then normalized. Afternormalization, the melting curves typically start near zero and plateaunear one after the melting transition. The derivatives of thenormalized, background-corrected curves depicted in FIG. 12D werecalculated using a central differencing technique. All proteins (exceptthe low intensity lysozyme) show the expected melting transitions infamiliar format.

FIG. 13 depicts plots of the melting curve data of FIG. 12A afterremoving an exponentially decaying background and also removing signaldue to a locally constant rate of aggregation. Proteins often aggregateat higher temperatures, thereby sequestering various hydrophobicresidues that were previously exposed through unfolding. Withcorrection, the resulting data may represent a background- andaggregation-corrected unfolding curve. As discussed below, the unfoldingcurves shown in FIG. 13 have been normalized to a percentage range of0-100 by rescaling the corrected melting curve data by a maximum valueprior to taking the derivative.

The unfolding curve data of FIG. 13 may be obtained by revising themodel of the experimental curve of Equation 1 to account for anaggregation between protein unfolding regions:

F(T)=U(T)+A(T)+B(T)  Eq. 15

In Equation 15, U(T) represents an unfolding curve, A(T) an aggregationsignal, and B(T) background EO radiation. It has been observed that theunfolding curves U(T) are substantially flat at low and hightemperatures, A(T) has a substantially constant negative slope, and, asdiscussed above, the B(T) follows an exponential decay model. Theseproperties may be used to identify an remove the background signal B(T).Other processes for background removal are described below inconjunction with FIG. 22.

The background removal process may comprise identifying a firsttemperature T_(L) just below the start of the unfolding transition wherethe aggregation curve is zero and the unfolding curve is flat. At thispoint in the curve, the measured (negative) slope may be entirelyattributable to the slope of the background EO radiation signal (e.g.,B(T): F′(T_(L))=B′(T_(L))). The location of T_(L) may be detected in thelow temperature range using the same exponential deviation analysis asused for DNA melting to identify the temperature at which the rawfluorescence no longer exhibits a constant exponential decay rate. SeeEquation 11 discussed above.

A second temperature T_(R) may be identified in the region oftemperatures above the unfolding transition, in which A′(T) isapproximately constant, U′(T)=0 and B′(T) remains exponential. Theseconditions imply that U″(T)=0 and A″(T)=0; accordingly,F″(T_(R))=B″(T_(R)). The derivative of the exponential model of thebackground EO radiation signal may be expressed as:

B′(T)=Ce ^(a(T−T) ^(L) ⁾  Eq. 16

From the two temperature values (T_(R) and T_(R)) the values of C and ain Equation 16 may be found; C=B′(T_(L))=F′(T_(L)) since from Equation16 we have B″(T_(R))=F″(T_(R))=aCe^(a(T) ^(R) ^(−T) ^(L) ⁾ and dividingaCe^(a(T) ^(R) ^(−T) ^(L) ⁾=F″(T_(R))/F′(T_(L)) is an equation that canbe solved using e.g., Newton's method. Upon determining C and a, andhence B′(T), the background model may be subtracted from theexperimental melting curve data (See Equation 15,U′(T)+A′(T)=F′(T)−B′(T)) to obtain a derivative of thebackground-corrected melting curve.

The unfolding curve U(T) may then be extracted from the aggregationcurve A(T) (e.g., extracted from the background-corrected melting curvedata calculated above). In some embodiments, the aggregation correctionmay comprise fitting the derivative of the extracted unfolding andaggregation superposition by a logistic model of its higher temperaturerange aggregation component A′(T). Since the exponential background wasextracted above, it may be possible to measure a locally constant(negative) aggregation rate M in this range. The aggregation rate M maybe used as a “carrying capacity” of the logistic model:

$\begin{matrix}{{A^{\prime}(T)} = \frac{M}{\left( {1 + {D\; ^{kT}}} \right)}} & {{Eq}.\mspace{11mu} 17}\end{matrix}$

Next, exponential deviation analysis (as discussed above) may beperformed on the quantity

${A^{\prime}(T)} = {{\frac{- M}{A^{\prime}(T)} - 1} = {D\; ^{kT}}}$

to identify a fitting range on which the parameters D and k areconstant. The average values of D and k in this range may be used forthe fit. The resulting model aggregation curve may be subtracted fromthe background-corrected curve, resulting in a derivative of thebackground- and aggregation-corrected unfolding curve (See Equation 1,U′(T)=F′(T)−B′(T)−A′(T)). The background and aggregation derivativecurve U′(T) may be integrated to obtain a background- andaggregation-corrected melting curve.

In an alternative approach, the extracted unfolding and aggregationsuperposition by a logistic model of the lower temperature range isimplemented under the assumption that the effects of aggregation arenegligible at temperatures up to and/or including an upper shoulder ofthe extracted curve, (denoted as T_(S)). The T_(S) temperature mayidentified as the point at which the second derivative of U(T)+A(T) ismost negative (e.g., min {U″(T)+A″(T)}), such that U′″(T_(S))=0. Sincethe derivative superposition U′(T)+A′(T) has been extracted (e.g., thederivative of the background EO radiation signal B′(T) has beenremoved), we locate the temperature T_(S) at which its first derivativeis most negative.

The parameters of the logistic model may be expressed as follows:

$\begin{matrix}{{U(T)} = \frac{N}{\left( {1 + {P\; ^{r{({T - T_{s}})}}}} \right)}} & {{Eq}.\mspace{11mu} 18}\end{matrix}$

The parameters of Equation 18 may be determined by the fact thatU′″(T_(S))=0, and the values of U(T_(S)) and U′(T_(S)) are:

$\begin{matrix}{{P = {2 - \sqrt{3}}},{N = {\left( {3 - \sqrt{3}} \right){U\left( T_{s} \right)}}},{r = \frac{\left( {3 - \sqrt{3}} \right){U^{\prime}(T)}}{\left( {2 - \sqrt{3}} \right){U\left( T_{s} \right)}}}} & {{Eq}.\mspace{11mu} 19}\end{matrix}$

Since the aggregation signal A(T) may be negligible below T_(S), thederivative curve may be evaluated after background removal (discussedabove) to find U′(T_(S))=F′(T_(S))−B′(T_(S)). As above, U′(T) may beintegrated to obtain a background- and aggregation-corrected meltingcurve (a melting curve that only comprises the unfolding signal, U(T)).The resulting unfolding curve U(T) and its derivative U′(T) mayoptionally be normalized to the percentage range 0-100 by rescalingU(T)=100(U(T))/max{U(T)}.

FIG. 13 depicts normalized melting curve data obtained from the proteinmelting curves of FIG. 12A using the methods described above (e.g.,Equations 15-19). Accordingly, the FIG. 13 plots depict the unfoldingcomponents (U(T)) of the FIG. 12A melting curve data.

FIG. 14A shows another set of experimental protein melting curves. Themelting curve data depicted in FIG. 14A illustrates a serial 2-folddilution of purified IgG (available from Sigma-Aldrich®), which wasfirst dialyzed against PBS, then serially diluted to finalconcentrations of 12 mg/ml, 6 mg/ml, 3 mg/ml, 1.5 mg/ml, 0.75 mg/ml and0.37 mg/ml in the presence of 10 uM 1-anilino-8-naphthaline sulfonate(available from Sigma-Aldrich®). Ten ul volumes were analyzed on aLightScanner® (available from Idaho Technology) modified for UVexcitation at 400 nm and melting curves collected at 1° C./min. Themelting curve data was then smoothed as described above.

FIG. 14B depicts plots of the experimental melting curve data of FIG.14A. The derivatives were approximated using a central differencingtechnique.

FIG. 14C depicts plots of the deviation function E(T) of the meltingcurve data of FIG. 14A. In the FIG. 14C example, the backgroundfluorescence signal was modeled using the quadratic polynomial ofEquation 13, and the window width was set to thirty data points. Thedeviation function was formed from the constants multiplying thequadratic term of each fit (a_(i) in Equation 13).

FIG. 14D depicts derivative plots of the background-corrected meltingcurves of FIGS. 14A-14C. The background-corrected melting curve data wasobtained by manually setting background removal cursor locations at 80.0degrees and 98 degrees (both located in the background region above themelting features for all samples). For each melting curve, a quadraticpolynomial was fit to the smoothed melting curve data (using aleast-squares technique) within the cursor locations. Abackground-corrected melting curve was formed by subtracting the modelof the background fluorescence signal from the smoothed experimentalmelting curve data. The background-corrected melting curves were thennormalized. Derivatives of the normalized, background-corrected curveswere approximated using a central differencing technique. Although FIGS.12D and 14D describe a manual background correction technique, theteachings of the disclosure may be used to automatically calculatebackground-corrected melting curve data as described below inconjunction with FIGS. 18-21 and Equations 20-27.

FIG. 15 depicts derivative normalized unfolding curve data of theprotein melting curves of FIG. 14A obtained using the methods describedabove (e.g., Equations 15-19). Accordingly, the FIG. 15 plots depict theunfolding components (U(T)) of the FIG. 14A melting curve data.

FIG. 16 depicts one embodiment of a method 1600 for identifying negativesamples. As used herein, a negative sample may refer to an experimentalmelting curve F(T) that does not include a valid melting transitionregion. A set of melting curves may include one or more negative controlsamples that serve to validate the results. Alternatively, or inaddition, negative samples may be caused by an error in performing themelting curve experiment, an error in PCR processing, an error inmeasuring the raw fluorescent values comprising the melting curve, anerror relating to the binding dye used in the particular experiment, theabsence of a nucleic acid product, or the like. It may be desirable todetect negative samples for validation purposes and/or to cut down onprocessing time and/or to avoid other problems that may arise fromprocessing invalid data.

At step 1610, the method 1600 may be initialized, which, as discussedabove, may comprise loading one or more computer-readable instructionscomprising the method 1600, accessing one or more hardware components,and the like.

At step 1620, an experimental melting curve F(T) may be accessed.

At step 1630, the experimental melting curve F(T) may be used togenerate a deviation function E(T). The deviation function E(T) may begenerated according to Equations 9-14 described above and/or the method300 of FIG. 3. Therefore, the method 1600 may be configured to access adeviation function E(T) generated using the method 300 and/or mayincorporate one or more steps of the method 300.

At step 1640, the deviation function E(T) may be analyzed to determinewhether it includes a valid melt transition region. As discussed above,the deviation function E(T) may quantify the extent to which theexperimental melting curve F(T) deviates from a model of the backgroundflorescence B(T) (e.g., in terms of a deviation between respectiveexponential decay factors). In temperature regions where F(T)corresponds to the background model (e.g., in background areas), thedeviation function E(T) is small, whereas in a melting region, thedeviation function E(T) increases. Therefore, the deviation functionE(T) may be used to identify which portions of the experimental meltingcurve F(T) correspond to melt transition regions and which arebackground. This determination may comprise comparing the deviationcurve E(T) to a threshold. The threshold may be set such that deviationvalues less than the threshold are indicative of a background region,and deviation values greater than the threshold are indicative of amelting region. An example of a deviation threshold is provided below inconjunction with FIG. 19. In some embodiments, the threshold may becalculated as a ratio of a deviation function E(T) peak to a deviationfunction E(T) average. Alternatively, or in addition, the threshold maybe derived from analysis of a set of melting curves (e.g., a ratio ofthe average deviation function E(T) peak). For example, a mean μ andstandard deviation σ of the deviation function E(T) of a set of meltingcurves may be calculated. Those curves that differ from the group bymore than a particular amount (e.g., two (2) standard deviations σ) maybe culled from the analysis. The remaining melting curves F(T) may beused to calculate an “average maximum,” which may form the basis of abackground/melting region threshold (e.g., as 1/e, 1/3, or some otherratio of the maximum value, or the like).

At step 1640, if the analysis of the deviation function E(T) indicatesthat the experimental melting curve F(T) does not contain a valid melttransition region (e.g., is below a threshold for all values of T) theflow may continue to step 1650; otherwise, the flow may continue to step1660.

At step 1650, the experimental melting curve F(T) may be marked as anegative sample. Step 1650 may comprise removing the melting curve froma set of melting curves to be processed and/or flagging the experimentalmelting curve F(T) as an “invalid” or “negative” sample. In someembodiments, the set of experimental melting curves F(T) may compriseone or more known “negative controls.” These may be experimental meltingcurves that are configured to exhibit characteristics indicative of anegative sample and, as such, may be used to validate the results. Step1650 may, therefore, comprise comparing an identifier of the negativesample to a list of known “negative controls” to determine whether thenegative sample is a “negative control.”

In some embodiments, at step 1660, the experimental melting curve F(T)may be marked as a “valid” melting curve. In other embodiments, themarking of step 1660 may not be performed (e.g., any experimentalmelting curve F(T) remaining in the set and/or that is not marked as“invalid” may be considered to be valid).

FIG. 17 depicts an alternative embodiment of a method 1700 for detectingnegative samples. At steps 1710, 1720, and 1730 the method 1700 isinitialized, melting curve data is accessed, and a deviation function iscomputed as described above in conjunction with steps 1610-1630.

At step 1732, a minimum value (min_(E)) of the absolute value of thedeviation function E(T) within the temperature region [T_(MIN),T_(MAX)−W] may be determined. The minimum value min_(E) value may besubtracted from E(T) for all values of T within the range [T_(MIN),T_(MAX)−W], yielding E_(m)(T):

E _(m)(T)=|E(T)|−min_(E)  Eq. 20

At step 1734, a maximum value max_(E) and an average or mean value μ_(E)of the modified deviation function E_(m)(T) may be calculated.

At step 1736, a ratio R_(E) of the maximum value max_(E) to the averageor mean value μ_(E) of the modified deviation function E_(m)(T) may becalculated:

$\begin{matrix}{R_{E} = \frac{\max_{E}}{\mu_{E}}} & {{Eq}.\mspace{11mu} 21}\end{matrix}$

At step 1740, the method 1700 determines whether the curve is a negativesample using the ratio R_(E). In some embodiments, step 1740 maycomprise comparing the ratio R_(E) calculated at step 1736 to athreshold value. The threshold value may be defined by a user of themethod 1700 and/or may be a pre-determined value. For example, forautomatic high-resolution melting curve analysis, the threshold valuemay be five (5). A ratio R_(E) less than the threshold may be indicativethat no melting region exists and, as such, the melting curve F(T) is anegative sample, and the flow may continue at step 1750; otherwise, theflow may continue at step 1760. At step 1750, the melting curve F(T) maybe marked as an invalid or negative sample as described above inconjunction with step 1650. At step 1760, the melting curve F(T) may bemarked as a valid sample as described above in conjunction with step1660.

FIG. 18 depicts one embodiment of a method 1800 for automaticallyidentifying background and/or melting regions of a melting curve usingdeviation analysis. As will be discussed below, the temperature regionsidentified using method 1800 may be used to seed an automated backgroundsubtraction process and/or for display or other processing of themelting curve data.

At step 1810, the method 1800 may be initialized, which, as discussedabove, may comprise allocating and/or initializing resources required bythe method 1800, loading one or more instructions and/or distinctsoftware modules from a computer-readable storage medium, accessinghardware components, or the like.

At step 1820, the method 1800 may access an experimental melting curveF(T), which may comprise a set of raw fluorescence measurements as afunction of temperature. The experimental melting curve F(T) may includea background fluorescence component B(T) and, as such, may be modeled asa sum of the background fluorescence B(T) and a “true” melting curvefluorescence M(T). See Equation 1 discussed above. In some embodiments,the accessing of step 1820 may further comprise accessing and/orcalculating a normalized experimental melting curve F(T).

At step 1830, a deviation function E(T) may be generated. The deviationfunction may be generated using method 300 described above. Therefore,step 1830 may comprise accessing a deviation function E(T) generated byan external process (e.g., method 300), and/or step 1830 may incorporateone or more steps disclosed in the method 300.

At step 1840, the deviation function may be used to identify a searchregion for the normalization cursors and, by extension, a meltingtransition within the melting curve F(T). As discussed above, the searchregion may comprise background regions of the melting curve F(T), whichmay bracket a melting transition region (e.g., comprise a low backgroundregion before a melting transition and a high background region afterthe melting transition). Therefore, the identifying of step 1840 maycomprise identifying a low search region T_(low) and a high searchregion T_(high). Identifying the low search region T_(low) and highsearch region T_(high) may, by extension, identify a melting transitionregion therebetween (e.g., the temperature region between above lowregion T_(low) and below high region T_(high)).

The deviation function generated at step 1830 may be used to identifythe temperature regions of interest (e.g., the low background region,the high background region, and/or the melting region therebetween).Identifying these temperature regions may comprise comparing thedeviation function E(T) of step 1830 to one or more thresholds. Asdescribed above, regions of high deviation may be indicative of amelting region, and areas of low deviation may be indicative of abackground region. Therefore, the identifying of step 1830 may comprisecomparing the deviation function E(T) to one or more thresholds,computing an average and/or ratio of a peak of the deviation functionE(T) to a mean or average thereof, or the like.

Although method 1800 discusses identifying a single pair of temperatureregions (T_(low), T_(high)) bracketing a single melting transition, oneskilled in the art would recognize that the method 1800 could be adaptedto identify any number of temperature regions (T_(low), T_(high))according to the number of melting transitions within the melting curvedata. One example of a method 2000 for identifying multiple meltingregions is described below in conjunction with FIG. 20. Therefore, thisdisclosure should not be read as limited to identifying any particularnumber of search regions and/or melting transitions within a meltingcurve.

FIG. 19 depicts one example of a deviation plot of several exemplarydeviation functions. In the FIG. 19 example, a single melting transition1920 is depicted. Therefore, two (2) temperature regions (T_(low) 1930and T_(high) 1932) may be identified at step 1840. Other experimentalmelting curves may include additional melting transition regions (e.g.,may include n melting transition regions). Therefore, the identificationof step 1840 may comprise identifying n background temperature regionswithin a melting curve (e.g., T_(low) _(_) ₁ and T_(high) _(_) ₁,T_(low) _(_) ₂ and T_(high) _(_) ₂, . . . , T_(low) _(_) _(n) andT_(high) _(_) _(n)). Each of the melting transition regions may includemultiple melting patterns, each corresponding to a different genotype,for example 1912, 1914, and 1916. However, outside of each meltingregion, the melting curves of the different genotypes are similar. Thisallows one melting region (with flanking backgrounds) to be defined formultiple curves. Since melting analysis compares multiple curves, it isoften advantageous to use these aggregate regions rather than individualregions for each curve.

The temperature regions T_(low) 1930 and T_(high) 1932 may be selectedusing, inter alia, the deviation function E(T) of step 1830. Thedeviation function E(T) of a melting curve may be compared to one ormore deviation thresholds within the temperature range [T_(min),T_(max)] or [T_(min), T_(max)−W] of the experimental melting curve F(T).In the FIG. 19 example, temperature regions where the deviation functionE(T) is less than the threshold 1910 may be identified as backgroundregions 1930 and 1932, whereas regions where the deviation function E(T)exceeds the threshold 1910 may be identified as a melting region 1920.

The threshold 1910 may be pre-determined. Alternatively, or in addition,the threshold 1910 may be calculated by averaging the deviationfunctions E(T) of a plurality of experimental melting curves F(T) and/orusing a peak value of a deviation function E(T). The averaging and/orratio calculation may comprise outlier rejection and/or otherstatistical techniques (e.g., negative sample identification discussedabove). For example, a mean μ and standard deviation σ of the deviationfunction E(T) of the set of melting curves may be calculated. Thosecurves that differ from the group by more than a particular amount(e.g., two (2) standard deviations σ) may be culled from the analysis.The remaining melting curves F(T) may be used to calculate an “averagemaximum,” which may form the basis of the threshold 1910 (e.g., as 1/e,1/3, or some other ratio of the maximum value, or the like).

FIG. 19 shows three (3) exemplary deviation functions E(T): 1912, 1914,and 1916. The melting region 1920 is depicted as a region where thedeviation functions E(T) 1912, 1914, and/or 1916 exceed the deviationthreshold 1910. A lower background region 1930 comprises the temperatureregion wherein the deviation functions E(T) 1912, 1914, and/or 1916 fallbelow the deviation threshold 1910.

Referring back to FIG. 18, at step 1880, the temperature region(s)identified at step 1840 may be made available for display and/or furtherprocessing. As described, step 1880 may comprise storing the identifiedtemperature region(s) on a computer-readable storage medium, displayingthe regions on an HMI (e.g., overlaying the regions on a display ofmelting curve data), using the regions to display a portion of a meltingcurve data (e.g., displaying only a melting region of the data),transmitting the data to an external system and/or process (e.g., anexponential background removal process), or the like.

FIG. 20 is a flow diagram of another embodiment of a method 2000 forautomatically identifying background and/or melting transition regionswithin a melting curve. The method 2000 of FIG. 20 may be adapted toidentify melting regions within a melting curve comprising multiplemelting regions: an amplicon melting region and a probe melting region.As will be discussed below, in this exemplary implementation, theamplicon melting region may be more pronounced than the probe meltingregion. Analysis of a melting curve of this type (e.g., comprisingamplicon and probe melting regions) may allow for simultaneous mutationscanning and genotyping. However, the teachings of method 2000 could beapplied to other melting curves comprising different sets of meltingregions. Therefore, method 2000 should not be read as limited in thisregard.

As discussed above, the melting curves processed by the method 2000 mayinclude two (2) melting regions (e.g., amplicon and probe meltingregions). An example of a deviation function plot of such a meltingcurve is provided in FIG. 21A. The method 2000 may be configured toautomatically identify four (4) distinct temperature values: a lowamplicon temperature value T_(A,L) and a high amplicon temperature valueT_(A,H) to bracket the amplicon melting region, and a low probetemperature value T_(P,L) and a high probe temperature value T_(P,H) tobracket the probe melting region. The temperatures values are identifiedsuch that T_(P,L)<T_(P,H)<T_(A,L)<T_(A,H).

At steps 2010 and 2020, the method 2000 may be initialized and accessmelting curve data as described above.

At step 2030, a deviation function E(T) of the melting curve data may begenerated. The deviation function E(T) may be generated using method 300and/or by incorporating one or more steps of method 300.

At steps 2032 and 2034, a minimum value min_(E) of the absolute value ofthe deviation function E(T) within the temperature range [T_(min),T_(max)−W (temperature window width)] is determined. The minimum valuemin_(E) may be subtracted from E(T) for all values of T within [T_(min),T_(max)−W], yielding E_(m)(T) (where E_(m)(T)=|E_(T)(T)|−min_(E)). Amaximum value max_(E) of E_(m)(T) may be determined as described abovein conjunction with steps 1732-1734 of FIG. 17.

At step 2040, the first set of temperatures is determined. The first setof temperature cursors may comprise a low amplicon cursor T_(A,L) and ahigh amplicon cursor T_(A,H) bracketing an amplicon melting region. Thelow amplicon cursor T_(A,L) may be the smallest value of T (within thetemperature range of E_(m)(T)) where the absolute value of the deviationfunction E_(m)(T) is greater than or equal to a particular value. Insome embodiments, the value may be max_(E) scaled by a scaling factor(e.g., 1/e, 1/3, or another scaling factor). Accordingly, thetemperature T_(A,L) may be identified as the lowest temperature Tsatisfying Equation 22:

$\begin{matrix}{T_{A,L} \equiv {\min\limits_{T}\left\{ {{{E_{m}(T)}} \geq \frac{\max_{E}}{}} \right\}}} & {{Eq}.\mspace{11mu} 22}\end{matrix}$

One example of identifying T_(A,L) in this way is provided in FIG. 21A,which shows T_(A,L) 2124.

The high amplicon temperature value T_(A,H) may be identified as thelargest value of T (within the temperature range of E_(m)(T)) where theabsolute value of the deviation function E_(m)(T) is greater than orequal to a particular value (e.g., max_(E) scaled by a constant, such as1/e):

$\begin{matrix}{T_{A,H} \equiv {\min\limits_{T}\left\{ {{{E_{m}(T)}} \geq \frac{\max_{E}}{}} \right\}}} & {{Eq}.\mspace{11mu} 23}\end{matrix}$

One example of identifying T_(A,H) in this way is provided in FIG. 21A,which shows T_(A,H) 2126.

In some embodiments, at step 2050, the first set of temperatures T_(A,L)and T_(A,H) identified at step 2040 may be modified. The analysis may beimproved by using temperature values outside of the values T_(A,L) andT_(A,H). Therefore, respective buffer values B_(A,L) and B_(A,H) may beincluded on either side of the temperatures T_(A,L) and T_(A,H) usingbuffer constants B_(A,L) and B_(A,H), the value of which may beempirically determined. The buffer constants may be selected to be closeto a feature size of interest within the melting curve data (e.g., 1°C.). The temperature locations, therefore, may be modified to beT_(A,L)−B_(A,L) and T_(A,H)+B_(A,H), respectively. See FIG. 21A. Inaddition, and as depicted on FIGS. 21A and 21B, background temperatureregions based on the values T_(A,L) and T_(A,H) may be defined by addinga W parameter on either side thereof.

As will be discussed below, the temperature values T_(A,L) and T_(A,H)and/or temperature region defined thereby, may be used to identifybackground and/or melting regions, automate an exponential backgroundsubtraction process (e.g., the temperatures may be used to construct anexponential model of the background fluorescence per Equations 2-6),used in a clustering or scanning operation, or the like.

At step 2060, a probe temperature region within E_(m)(T) may beidentified. The temperature region may comprise the temperature rangebelow the lower temperature (T_(A,L)) of the first set of temperatures.In some embodiments, the temperature region may be lower than T_(A,L), abuffer value, and/or the width of the deviation function E(T)temperature windows T_(W) (e.g., all T of E_(m)(T) belowT_(A,L)−B_(A,L)−W). This temperature region may include the secondmelting region (probe melting region) and exclude the amplicon meltingregion. See FIG. 21B. As shown in FIG. 21A, the probe melting region maybe less pronounced than the amplicon melting region (as quantified byE_(m)(T)). For this reason, the second set of temperatures (e.g., theprobe temperatures T_(P,L) and T_(P,H)) may be identified after thefirst set of temperatures (T_(A,L) and T_(A,H)) and using a sub-set ofE(T). However, in other embodiments and/or in other melting curve types,this may not be the case. Therefore, this disclosure should not be readas limited to any particular order and/or number of temperature sets.

At step 2062, a minimum value min_(E) of E(T) within the regionidentified at step 2060 may be determined. See step 2032 discussedabove. The min_(E) value may be used to generate E_(m2)(T) within thetemperature region (referred to herein as E_(m2)(T) to be distinguishedfrom E_(m)(T) discussed in steps 2032-2050).

At step 2064, a maximum value max_(E2) of E_(m2)(T) may be determined.See step 2034 discussed above; see also point 2142 on FIGS. 21A and 21B.

At step 2070, the second set of temperatures may be identified using themaximum value max_(E2) determined at step 2064. A low temperature valueT_(P,L) of the second set of temperatures may be the lowest temperaturewithin the temperature region where the value of E_(m2)(T) is greaterthan max_(E2) as scaled by a constant (e.g., 1/e). See T_(P,L) 2154 onFIG. 21B. A high temperature value T_(P,H) of the second set oftemperatures may be the highest temperature within the region where thevalue of E_(m2)(T) is greater than or equal to max_(E2) as scaled by aconstant (e.g., 1/e). See T_(P,H) 2156 on FIG. 21B.

At step 2080, the second set of temperatures T_(P,L) and T_(P,H) may bemodified using respective buffer constants and/or a width of thetemperature window W used to generate the deviation function E(T). SeeStep 2050 discussed above; see also points 2164 and 2166 on FIG. 21B.

At step 2090, the first and the second sets of temperatures may be madeavailable for display and/or use in one or more external processes. Insome embodiments, and as discussed below, the temperature sets may beused to automate an exponential background subtraction process. Forexample, the first set of temperatures (T_(A,L) and T_(A,H)) may be usedto subtract background in the amplicon melting region, and the secondset of temperatures (T_(P,L) and T_(P,H)) may be used to subtractbackground fluorescence in the probe melting region. See Equations 1-6discussed above. Alternatively, or in addition, the sets of temperaturevalues may be used to automatically provide for the display and/orprocessing of the amplicon and/or probe melting regions (e.g.,automatically display a scaled and/or zoomed view of the respectivemelting region(s), provide for automated clustering within the relevantregion(s), and so on).

FIGS. 21A and 21B are plots of an exemplary deviation function E_(m)(T)2110 generated using a melting curve comprising an amplicon meltingregion and a probe melting region.

FIG. 21A shows the operation of steps 2030-2050 of method 2000 describedabove. For example, 2112 shows a maximum value max_(E) of E_(m)(T), 2114is maximum value max_(E) scaled by a scaling factor (1/e), and 2124 isthe lowest temperature T_(A,L) at which E_(m)(T) is greater than orequal to max_(E)/e, and 2126 is the highest temperature T_(A,H) at whichE_(m)(T) is greater than or equal to max_(E)/e. As shown in FIG. 21A,the temperature values T_(A,L) and T_(A,H) may be modified by respectivebuffer constants 2134 and 2136 and/or the temperature window width W.

FIG. 21B shows the operation of steps 2060-2080 of method 2000 describedabove. The plot 2140 includes a probe melting region, which may comprisea sub-set of the temperature range of E_(m)(T) (e.g., the temperaturerange below T_(A,L)−B_(A,L)−W). The function E_(m2)(T) is generated bysubtracting the minimum value min_(E) of the absolute value of thedeviation function for all values of E(T) within a probe melting region(e.g., the temperature range identified at step 2050). The maximum valuemax_(E2) of E_(m2)(T) 2142 may be used to identify temperature valuesT_(P,L) 2154 and T_(P,H) 2156. The low probe temperature T_(P,L) 2154 isidentified as the lowest temperature at which E_(m2)(T) is greater thanor equal to the scaled maximum value max_(E2)(max_(E2)/e), and the highprobe temperature T_(P,H) 2156 is identified as the highest temperatureat which E_(m2)(T) is greater than or equal to the scaled maximum valuemax_(E2)(max_(E2)/e). As shown in FIG. 21B, the temperatures T_(P,L) andT_(P,H) may be modified using respective buffer constants 2164 and 2166and/or the temperature window width W.

As discussed above, the temperature values identified in the method 2000may be used to subtract a background fluorescence signal B(T) from anexperimental melting curve. This may be done using the backgroundtemperature values identified in the method 2000 (e.g., the temperaturevalues bracketing the amplicon and probe melting regions). Thetemperature values so identified may be used to model an exponentialbackground signal per Equations 2-5. The model of the exponentialbackground may be subtracted from the experimental melting curve F(T)per Equation 6.

FIG. 21C illustrates another example of a process for identifying acursor probe region. The deviation function 2160 may correspond to aprotein melting curve. The exemplary deviation function 2160 includes abaseline deviation 2165, a melting region, and a background region(depicted as a cursor probe region 2175). The melting region of thedeviation function 2160 may include multiple melting patterns, eachcorresponding to a different protein. However, in the cursor proberegion 2175 (outside of the melting region(s)), the melting curves ofdifferent proteins are similar. This allows one background region(cursor probe region 2175) to be used with multiple curves. Sincemelting analysis compares multiple curves, it may often be advantageousto use an aggregate region (e.g., region 2175) rather than individualregions for each curve.

The cursor probe region 2175 may be identified by selecting a backgroundcursor temperature T_(c) 2174 (as in step 2040 of FIG. 20). Thebackground cursor temperature T_(c) 2174 may be identified as thehighest temperature along with deviation function 2160 (and within thetemperature range [T_(min),T_(max)] or [T_(min), T_(max)−W]) that isgreater than and/or equal to a deviation threshold 2164. The deviationthreshold may be defined as a ratio of a maximum value max_(E) 2162 (ora spread between the maximum value max_(E) 2162 and a baseline deviation2165) and a constant (e.g., e). See Equations 22 and 23 above. Asillustrated in FIG. 21C, the cursor probe region 2175 may be defined ascomprising temperatures that are greater than and/or equal to thebackground cursor temperature T_(c) 2174.

As discussed above, the value of the deviation threshold 2164 may bepre-determined (a constant) and/or using a maximum value max_(E) 2162 ofa deviation function E(T). Alternatively, or in addition, the threshold2164 may be calculated by averaging the deviation functions E(T) of aplurality of experimental melting curves. The averaging and/or ratiocalculation may comprise outlier rejection and/or other statisticaltechniques (e.g., negative sample identification discussed above). Forexample, a mean μ and standard deviation σ of the deviation functionE(T) of the set of melting curves may be calculated. Those curves thatdiffer from the group by more than a particular amount (e.g., two (2)standard deviations σ) may be culled from the analysis. The remainingmelting curves F(T) may be used to calculate an “average maximum,” whichmay form the basis of the threshold 2164 (e.g., as 1/e, 1/3, or someother ratio).

The background temperature regions (cursor probe regions) identified inmethods 1800 and/or 2000 and/or using FIGS. 21A-21C may be used toautomate a background correction process. FIG. 22 is a flow diagram of amethod 2200 for automating exponential background subtraction usingdeviation analysis.

At steps 2210-2230 the method 2200 may be initialized, access meltingcurve data, and generate a deviation function E(T) therefrom asdescribed above.

At step 2240, background temperature regions within the melting curvedata may be identified. The background temperature regions may beidentified using method 1800 of FIG. 18 (by comparing the deviationfunction E(T) to one or more threshold values). Alternatively, or inaddition, the background regions may be identified according to method2000 (e.g., using a scaled maximum value of the deviation functionE(T)).

At step 2250, an objective function (Φ) may be accessed. The objectivefunction Φ may define the desirability of a particular solution to anoptimization problem, such as, in the case of method 2200, the locationof the cursor locations used to model the exponential backgroundfluorescence B(T) in an experimental melting curve F(T). In someembodiments, the objective function Φ accessed at step 2250 may be ofthe following form:

$\begin{matrix}{\min\limits_{T_{L},{T_{R} \in \Re}}{\Phi \left( {T_{L},{T_{R}{F(T)}}} \right)}} & {{Eq}.\mspace{11mu} 24}\end{matrix}$

In Equation 24, T_(L) and T_(R) represent the normalization cursorlocations (temperatures that bracket the melting region of the curve)along the temperature axis. The objective function of Equation 24 may besubject to certain conditions. For example, the search space for thenormalization cursor locations T_(L) and T_(R) may be confined to thetemperature regions identified at step 2240.

The objective function Φ may be configured to minimize the error betweenthe experimental melting curve F(T) and an ideal melting curve. FIG. 23Adepicts an example of an “ideal” melting curve and a portion of anormalized experimental melting curve F(T). As shown in FIG. 23A, bothcurves 2310 and 2312 comprise a low background region 2326, a highbackground region 2328, and a melting region 2325. In the ideal meltingcurve 2310, the melting region 2325 is modeled as a smooth,monotonically non-increasing function. The ideal and experimentalmelting curves F(T) 2310 and 2312 are similar in the background regions2326 and 2328, but show deviation in the melting region 2325.Accordingly, the deviation between the ideal 2310 and the experimentalcurves 2312 may be used to distinguish the background regions 2326 and2328 from the melting region 2325 (e.g., by comparing the exponentialdecay rate of the ideal curve 2310 and the experimental curve F(T) 2312(e.g., as described above in conjunction with methods 1800 and 2000).

The curves 2310 and 2312 diverge within the region 2320, which is shownin an expanded view in FIG. 23B. The area 2322 shows a total difference(integrated over temperature T) between the ideal melting curve 2310 andthe normalized melting curve 2312. The temperature where the normalizedmelting curve F(T) 2312 crosses the fluorescence halfway point (0.5normalized fluorescence) may be defined as T_(1/2) 2324.

Although FIGS. 23A and 23B depict ideal and experimental melting curves2310 and 2312 comprising a single melting region 2325, this disclosureis not limited in this regard. As could be appreciated by one of skillin the art, the teachings of this disclosure could be applied to morecomplex melting curves comprising any number of melting regions (andcorresponding background regions).

In some embodiments, the objective function Φ accessed at step 2250 maybe configured to minimize error occurring before the T_(1/2) point 2324to one (1), and the error occurring after the T_(1/2) point 2324 to zero(0). In addition, the objective function Φ may be configured to causethe experimental melting curve F(T) to conform to a monotonicallydecreasing exponential function within the melt transition region (e.g.,region 2325 of FIG. 23).

Referring back to FIG. 22, the objective function Φ accessed at step2250 may be configured to search for temperature cursor locations onlywithin temperature regions identified as “background.” The objectivefunction Φ may be re-written to include constraints to within low andhigh background regions:

$\begin{matrix}{\min\limits_{T_{L},{T_{R} \in \Re}}{{\Phi \left( {T_{L},{T_{R}{F(T)}}} \right)}\left\{ \begin{matrix}{{T_{L} \in T_{low}};{and}} \\{T_{R} \in T_{high}}\end{matrix} \right.}} & {{Eq}.\mspace{11mu} 25}\end{matrix}$

As discussed above, the objective function Φ may be configured tominimize error occurring before the melting transition (e.g., before theT_(1/2) point 2324 of FIG. 23) to one (1), and to minimize erroroccurring after the melting transition to zero (0). The objectivefunction of Equation 26 below is so configured:

$\begin{matrix}{{\Phi \left( {T_{L},{T_{R}{F(T)}}} \right)} = {{\int_{T_{L}}^{T_{1/2}}{\left\lceil {{\overset{\_}{F}(T)} - 1} \right\rceil_{0}\delta \; T}} + {\int_{T_{1/2}}^{T_{R}}{{\left\lfloor {\overset{\_}{F}(T)} \right\rfloor_{0}}\delta \; T}}}} & {{Eq}.\mspace{11mu} 26}\end{matrix}$

As used in Equation 26, the operator └α(T)┘₀, ┌α(T)┐₀ (e.g., as appliedto F(T)) has the following characteristics:

$\begin{matrix}{\left\lceil {\alpha (T)} \right\rceil_{0} = \left\{ {{\begin{matrix}{{{\alpha (T)} > 0}->{\alpha (T)}} \\{{{\alpha (T)} \leq 0}->0}\end{matrix}\left\lfloor {\alpha (T)} \right\rfloor_{0}} = \left\{ \begin{matrix}{{{\alpha (T)} < 0}->{\alpha (T)}} \\{{{\alpha (T)} \geq 0}->0}\end{matrix} \right.} \right.} & {{Eq}.\mspace{11mu} 27}\end{matrix}$

At step 2260, the method 2200 may use the objective function Φ toidentify optimal normalization cursor values. The identification of step2260 may comprise evaluating the objective function Φ at varioustemperature values within the T_(low) and T_(high) temperature regions.In some embodiments, the regions may be quantized into a pre-determinednumber of values (e.g., 30 discrete temperature values within eachregion). The temperatures T_(L) and T_(R) that minimize the objectivefunction Φ may be identified as optimal cursor locations. Theidentification of step 2260 may include any optimization technique knownin the art, including local minima detection, steepest descent, gradientdescent, and the like.

At step 2270, the experimental melting curve F(T) may be processed toremove its background fluorescence B(T) component. See Equations 1-6discussed above. The removal of step 2270 may comprise modeling thebackground fluorescence using the optimal temperature values T_(L) andT_(R). The model may be subtracted from the melting curve data accordingto Equation 6 discussed above.

At step 2280, the “true” melting curve data M(T) may be made available,which as discussed above may comprise providing for displaying thecorrected data, storing the data in a computer-readable storage media,transmitting the data to another processor and/or system, or the like.

FIG. 24 is another embodiment of a method 2400 for automating backgroundfluorescence compensation. The method 2400 may include feedback andevaluation steps to allow for improvement to background subtractionresults.

The steps 2410-2470 may be implemented similarly to steps 2210-2270described above in conjunction with method 2200.

At step 2472, the processed and/or normalized melting curve data M(T)may be used for further analysis, e.g., may be displayed within an HMIor used in a genotyping operation, a scanning operation, clusteringprocess, grouping process, or the like.

The quality of the results of the analysis performed at step 2472 may bequantifiable. For example, if the analysis of step 2472 comprises aclustering or grouping operation, the separation between clusters/groupsmay be evaluated to determine a “quality” of the operation. Therefore,at step 2480, a quality metric may be calculated. The quality metric maybe used to quantify the quality of the background removal of step 2460(e.g., quantify the quality of the “optimal” cursors T_(L) and T_(R)).

Equation 28 illustrates one way of quantifying the quality of aclustering and/or grouping operation:

$\begin{matrix}{{\gamma (T)} = \frac{{{\mu_{1}(T)} - {\mu_{2}(T)}}}{\sqrt{{\sigma_{1}^{2}(T)} + {\sigma_{2}^{2}(T)}}}} & {{Eq}.\mspace{11mu} 28}\end{matrix}$

As shown in Equation 28, the quality metric γ is a function oftemperature. Equation 28 quantifies the quality of two clusters/groupsas a function of the separation between groups and cohesion withingroups (the groups are identified in Equation 28 as group one (1) andtwo (2)). The quality of a group/cluster is determined by the separationof the group mean values as well as a sum of the individual groupvariances. A low quality metric γ results from high deviation within thegroups one (1) and two (2) and/or small separation between the groupmeans. Alternatively, a “good” quality metric γ results if the groupsare tightly clustered (the values of σ₁ ²(T) and σ₂ ²(T) are small)and/or the groups are widely separated (the difference betweenμ₁(T)−μ₂(T) is large).

Although one example of a quality metric is discussed herein, oneskilled in the art would recognize that any quality metric (dependentupon any set of factors related to the analysis of step 2472) could beused under the teachings of this disclosure.

At step 2482, the quality metric calculated at step 2480 may beevaluated. The evaluation may determine whether to perform furtherrefinement on the melting curve data (e.g., by modifying the backgroundremoval cursor locations at step 2484). Therefore, step 2482 maycomprise comparing the quality metric to one or more thresholds.Alternatively, or in addition, the determination of step 2482 maycomprise comparing a current quality metric to a quality metric obtainedin one or more previous iterations of steps 2460-2480. If the metricshows consistent improvement (e.g., is following an improvementgradient), it be may determined that continued refinement may bedesirable, whereas if the quality metric is decreasing (e.g., for apre-determined number of iterations), continued refinement may beunlikely to cause improvement. Additionally, the determination mayinclude evaluating a maximum iteration counter or other processinglimit. If it is determined that further refinement of the cursorlocations is to be performed, the flow may continue at step 2484;otherwise, the flow may continue at step 2490.

At step 2484, the normalization cursor locations may be refined. Therefinement applied at step 2484 may be application specific (e.g.,defined by the analysis performed at step 2472). Alternatively, or inaddition, the refinement may comprise performing one or morepredetermined and/or user selectable shifts in cursor locations. In someembodiments, the quality metric calculated at step 2480 may determinethe refinement. Alternatively, or in addition, the refinements to thecursor locations T_(L) and T_(R) may be made in accordance with apre-determined pattern and/or may comprise a random component. Therefinement of step 2484 may further comprise evaluating the objectivefunction Φ using the refined cursor locations. If a change would resultin a poor result from the objective function Φ, the change may bediscarded in favor of another change that yields a better result. Afterrefining the cursor locations, the background removal, analysis, qualitymetric calculation, and evaluation of steps 2460-2482 may be performed.

At step 2490, the analysis results and/or processed melting curve datamay be made available. As discussed above, making data available maycomprise displaying the data on an HMI, storing the data in acomputer-readable storage medium, transmitting the data to anotherprocess and/or system, or the like.

It has been found that deviation plots of low temperature meltingtransitions or transitions over a wide temperature range are ofteneasier to automatically cluster correctly than other kinds of plots. Forexample, the human single base variant rs #729172, an A>C transversion,was amplified and genotyped using snapback primers. Snapback primers arethe subject of PCT Publication No. WO2008/109823, which is incorporatedby reference in its entirety. Additional information regarding snapbackprimers is available in Zhou L. et al., Snapback Primer Genotyping withSaturating DNA Dye and Melting Analysis, 54(10) Clin. Chem. 1648-56(October 2008).

In one example, different genotypes clustered correctly after deviationanalysis, but not after exponential background subtraction. Thefollowing primers were used to amply a 162 bp product from human genomicDNA:

(Seq. ID No. 6) ATGGCAAGCTTGGAATTAGC;  and (Seq. ID No. 7)ggTCTGCAGACCGAATGTATGCCTAAGCCAGCGTGTTAGA

The underlined bases in sequences 6 and 7 above are homologous to thehuman DNA target, the upper case bases that are not underlinedconstitute the probe element of the snapback primer, the bold base is atthe position of the single base variant, and the lower case bases are atwo (2)-base overhang mismatched to the target. The PCR was performed in10 μl reaction volumes in an LC480 real-time instrument (available fromRoche Applied Science) in the presence of 0.5 μM limiting primer, 0.05μM snapback primer, 3 mM MgCl₂, 50 mM Tris, pH 8.3, 500 μg/ml BSA, 1×LCGreen Plus, 200 μM each dNTP and 5 ng/μl human genomic DNA with 0.04U/μl KlenTaq 1 polymerase (AB Peptides). The reaction mixture was heatedto 95° C. for 2 min and then cycled for 50 cycles between 95° C. at 4.4°C./s with a 10 s hold, 58° C. at 2.2° C./s with a 10 s hold, and 76° C.at 4.4° C./s with a 15 s hold. This was followed by a melting protocolof heating to 95° C. at 4.4° C./s with a 10 s hold, cooling to 42° C. at2.2° C./s with a 1 s hold, and heating to 98° C. at 0.1° C./s withfluorescence monitoring at 10 acquisitions/° C.

The temperature interval of the snapback probe melting transition wasidentified manually by inspection of the melting curves and processedtwo (2) ways. FIG. 25 depicts deviation plots clustered automatically byunbiased hierarchal methods described in PCT Publication No.WO2007/035806, which was incorporated by reference above.

Although not used in this example, the clustering results depicted inFIG. 25 could be used to refine the background subtraction and/ortemperature region identification using quality assessment and feedbacktechniques. One example of a method for refining melting curve analysisusing such techniques is described above in conjunction with FIG. 24.See steps 2470-2482 of FIG. 29; see also Equation 20. For example, thequality metric of Equation 28 could be adapted to quantify the cohesionwithin and separation between the groups depicted in FIG. 25. Thequality metric could be assessed to determine whether the temperatureregions (used for background subtraction and/or temperature regionidentification) should be refined to yield better results (as quantifiedby the quality metric).

The clustering correctly separates the different genotypes, revealingthe expected homozygotes and heterozygotes with Tms at about 66 and 74°C., and identifying an unexpected heterozygote at a different Tm of 68°C. In contrast, if the same data are processed solely by exponentialbackground subtraction and displayed as a derivative plot, automaticclustering by exactly the same methods fails to distinguish the expectedheterozygotes (FIG. 26). The low temperature homozygote and theheterozygote cluster together, leading to incorrect genotyping. This ispresumably caused in part by increased dispersion of the curves within agenotype.

Deviation analysis can be used to identify negative samples (asdescribed above in conjunction with methods 1600 and 1700). In addition,deviation analysis may be used to automatically determine a probeanalysis region for clustering and genotyping. For example, methods 1800and 2000 automatically identify melting region(s) within melting curvedata using deviation analysis.

In one example, an F5 Leiden single base variant was genotyped by PCRand melting analysis using unlabeled probes, after the methods describedin Zhou L. et al., CT. High-resolution DNA Melting Analysis forSimultaneous Mutation Scanning and Genotyping in Solution, 51(10) Clin.Chem. 1770-77 (October 2005), which is hereby incorporated by referencein its entirety.

Samples were placed on a 96-well plate so that positive samples (of allthree genotypes) were interspersed with negative (no template control)samples in a checkerboard. After PCR and melting analysis, theunprocessed melting curves between 50 and 95° C. were accessed. FIG. 27depicts plots of the unprocessed melting curves so obtained.

As shown in FIG. 27, the curves segregate into two clusters, the topcluster of positive samples includes both unlabeled probe and PCRproduct melting transitions, while the lower cluster of negative samplesshows neither expected melting transition, although an unexpectedtransition around 75° C. is present from unintended amplification of analternative product. FIG. 28 shows a set of sample indicators detectedusing an amplitude cut off technique (shown as a straight line cut offin FIG. 27). As shown in FIG. 28, none of the negative samples wereaccurately identified using this technique.

The deviation function E(T) was generated for each of the melting curvesand used to automatically exclude negative samples (e.g., no-templatecontrol samples). As described above in conjunction with FIGS. 16 and17, negative sample identification may be used to exclude melting curvesthat fail to produce a signal that can be analyzed. In this example, thenegative sample identification was performed according to method 1700 ofFIG. 17 and, as such, comprised determining a minimum min_(E) of theabsolute value of the deviation function E(T) over the interval[T_(MIN), T_(MAX)−W], computing E_(M)(T) as described above (subtractingmin_(E) for all values of T), computing a maximum value max_(E) and meanor average of E_(M)(T), calculating a ratio of the maximum value max_(E)and mean or average, and comparing the ratio to a threshold, which, inthe example, was set to five (5).

FIG. 29 shows the set of melting curves of FIG. 27, wherein the negativesamples are removed. As shown in FIG. 29, the lower set of meltingcurves (the negative samples prominent in the lower portion of FIG. 27)are no longer included in the set of “valid” melting curves. FIG. 30shows a set of sample indicators comprising the negative samplesdetected using the deviation analysis technique described above. Bycomparison with FIG. 28 (negative sample identification using amplitudecut off), FIG. 30 shows that the use of deviation analysis allowed forthe successful identification of negative samples, which the amplitudecut off method failed to identify. The deviation analysis and amplitudecut off methods of negative sample identification may be implemented inparallel (e.g., simultaneously), since they are independent analyses.

After automatic exclusion of negative data, the deviation function wasfurther used to identify the PCR product (amplicon) melting region, theprobe melting region, and the entire region incorporating all meltingregions. In the example, and as described above in conjunction with FIG.20, four distinct temperatures were identified:T_(P,L)<T_(P,H)<T_(A,L)<T_(A,H). The lower temperature pair bracket theprobe melting region, T_(P,L)<T<T_(P,H), while the higher temperaturepair bracket the amplicon melting region, T_(A,L)<T<T_(A,H). In oneexample of automatic analysis of the full melting region forsimultaneous mutation scanning and genotyping, the extreme pair amongthese four temperatures, i.e., T_(P,L)<T<T_(A,H), can be used, so noadditional temperatures need be computed.

Although the amplicon region is identified by T_(A,L)<T<T_(A,H), theanalysis was started well outside of these limits using a buffer B oneach side of T_(A,L) and T_(A,H). Therefore, the region for analysisbecomes T_(A,L)−B<T<T_(A,H)+B. See step 2050 of FIG. 20.

The appropriate buffer values B were determined by the instrumentcharacteristics (noise, data density) and the minimum feature size to beextracted from the data, typically about 1° C. Furthermore, someanalysis methods (such as exponential background subtraction) require atemperature interval on each side for calculation, so an additionalwidth (W) may be included outside of each buffer zone to define theseintervals. See step 2050 of FIG. 20.

It is understood that each of the four 8 and W values may be the same ordifferent. When multiple melting curves are analyzed at once, theaverage or outermost intervals may be used.

After identifying the amplicon background and melting regions, atemperature range comprising the probe melting region is determined. Asdiscussed above, the temperature region comprises [T_(MIN),T_(A,L)−(B+W)] below the amplicon region. See step 2060 of FIG. 20.Within this temperature region, the minimum value min_(E2) of |E(T)|over the interval [T_(MIN), T_(A,L)−(B+W)] is identified, and a functionE_(M2)(T) is constructed over the interval [T_(MIN), T_(A,L)−(B+W)],(E_(M2)(T)=|E(T)|−min_(P)). See step 2062 of FIG. 20. A maximum valuemax_(E2) of E_(M2)(T) is determined. See Step 2064 of FIG. 20.

The probe temperature region identified above was evaluated to determinewhether a probe melting region exists (e.g., using negative sampleidentification as disclosed in methods 1600 and 1700 of FIGS. 16 and17). In this example, if a ratio of the probe to amplicon peaks on therespective deviation plots is less than about 0.02 (ifmax_(E2)<max_(E)/e⁴), it is determined that there is no automaticallydetectable probe melt in the data. It is understood that values otherthan 0.02 could be chosen, depending on the resolution of the instrumentused to acquire the melting curve data.

The probe temperature values (T_(P,L) and T_(P,H)) were identifiedaccording to method 1700 of FIG. 17. Therefore, identifying thetemperatures (T_(P,L) and T_(P,H)) comprised: if max_(E2) exceeds theabove threshold (max_(E)/e⁴), T_(P,L) is the smallest T in [T_(MIN),T_(A,L)−+(B+W)] for which E_(M2)(T)>max_(E2)/e. T_(P,L) is the largestTin [T_(MIN), T_(A,L)−(B+W)] for which E_(M2)(T)<max_(E2)/e. See step2070 of FIG. 20. Therefore, outside T_(P,L)<T<T_(P,H), the value ofE_(M2)(T)>max_(E2)/e, and this is the smallest subinterval of [T_(MIN),T_(A,L)−(B+W)] on which this statement holds.

The buffer (B) and width (W) intervals were used to expand the proberegion T_(P,L)<T<T_(P,H) to T_(P,L)−B<T<T_(P,H)+B orT_(P,L)−(B+W)<T<T_(P,L) (B+W) for probe analysis, similar to theamplicon analysis. See step 2080 of FIG. 20.

FIG. 31 shows the results of F5 probe analysis after automatic notemplate control exclusion, automatic identification of the amplicon andprobe regions, normalization of the probe region deviation data so thatsamples varied from one (1) to zero (0) on integrated deviation plots,clustering the curves for automatic genotyping, and plotting the probedata as an integrated deviation plot (as a percentage of cumulativedeviation). The plate map in FIG. 32 shows the correct pattern ofgenotype and negative control samples (negative samples identified usingthe deviation analysis techniques described above).

FIG. 33 is a block diagram of a system 3300 for analyzing melting curvedata. The system includes a computing device 3310, which may compriseone or more processors (not shown), memories (not shown),computer-readable media 3312, one or more HMI devices 3314 (e.g.,input-output devices, displays, printers, and the like), one or morecommunications interfaces 3316 (e.g., network interfaces, UniversalSerial Bus (USB) interfaces, etc.), and the like. Alternatively, or inaddition, the system 3300 may comprise a plurality of computing devices3310 in a local and/or distributed cluster (not shown).

The computing device 3310 may be communicatively coupled to a meltingcurve data source 3320, which may comprise a melting curve-generatinginstrument (e.g., a LightCycler® device available from RocheDiagnostics, GmbH, a HR-1™ high resolution melting instrument, or thelike). Alternatively, or in addition, the data source 3320 may comprisea computer-readable media comprising melting curve data.

The computing device 3310 may be configured to load computer-readableprogram code from the computer-readable media 3312. The program code maycomprise processor-executable or processor-interpretable instructionsimplementing one or more of the systems and methods disclosed herein(e.g., methods 300, 1600, 1700, 2000, 2200, 2400, and so on) or variantsthereof. The instructions may be embodied as one or more distinctsoftware modules on the computer-readable media 3312. The modules maycomprise a data acquisition module 3332 configured to access meltingcurve data from a data source 3320, a modeling module 3334 configured toaccess a model of background fluorescence, an analysis module 3336configured to perform deviation analysis on melting curve data (e.g.,generate a deviation function according to inter alia method 300 of FIG.3), a processing module 3338 configured to provide for display (via anHMI 3314) and/or further processing of the melting curve data using thedeviation analysis techniques described above (e.g., automated negativesample identification, exponential background subtraction, meltingregion identification, clustering, and the like), and a control module3339 configured to provide for control of the system 3300 by a humanuser (not shown) and/or by one or more external processes (not shown),such as another computing device or agent (not shown).

The control module 3339 may allow for directing the system 3300 toacquire and/or access melting curve data, to perform deviation analysison the melting curve data, and/or to display the analyzed data asdescribed above. For example, the control module 3339 may provide forthe display of melting curve data, clustering results, genotypingresults, scanning results, or the like on the HMI 3314. Therefore, thecontrol module 3339 may comprise a user interface (not shown) configuredto display user interface controls on and/or accept user input from theHMI 3314. In addition, the control module 3339 may be configured toaccept commands and/or instructions via one or more of thecommunications interfaces 3316 (e.g., from a remote computing device,agent, or the like). The control module 3339 may provide for acceptingprogramming commands from a user and/or external process to performautomated negative sample identification, melting region identification,background subtraction, display, clustering, and other processes. Thecontrol module 3339 may be further configured to store the results ofdeviation analysis processing in the computer-readable media 3312 and/ortransmit the results on one or more of the communications interfaces3316.

In some embodiments, the system 3300 may be configured to autonomouslyperform genotyping and/or scanning processes using the deviationanalysis techniques disclosed herein (e.g., methods 300, 1600, 1700,2000, 2200, 2400, or variants thereof). As discussed above, deviationanalysis techniques disclosed herein are not limited to any particularset of melting curve analysis applications, and the system 3300 could beconfigured to implement any number of melting curve analysisapplications using the deviation analysis techniques disclosed herein.Accordingly, neither this disclosure nor system 3300 should be read aslimited to any particular set of melting curve deviation analysisapplications.

The above description provides numerous specific details for a thoroughunderstanding of the embodiments described herein. However, those ofskill in the art will recognize that one or more of the specific detailsmay be omitted, or other methods, components, or materials may be used.In some cases, operations are not shown or described in detail.

Furthermore, the described features, operations, or characteristics maybe combined in any suitable manner in one or more embodiments. It willalso be readily understood that the order of the steps or actions of themethods described in connection with the embodiments disclosed may bechanged as would be apparent to those skilled in the art. Thus, anyorder in the drawings or Detailed Description is for illustrativepurposes only and is not meant to imply a required order, unlessspecified to require an order.

Embodiments may include various steps, which may be embodied inmachine-executable instructions to be executed by a general-purpose orspecial-purpose computer (or other electronic device). Themachine-executable instructions may be embodied on a computer-readablestorage medium. In some embodiments, the instructions may be embodied asone or more distinct software modules. Alternatively, one or more of thesteps may be performed by hardware components that include specificlogic for performing the steps, or by a combination of hardware,software, and/or firmware.

Embodiments may also be provided as a computer program product includinga computer-readable medium having stored instructions thereon that maybe used to program a computer (or other electronic device) to performprocesses described herein. The computer-readable medium may include,but is not limited to, hard drives, floppy diskettes, optical disks,CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs, EEPROMs, magnetic or opticalcards, solid-state memory devices, or other types ofmedia/machine-readable medium suitable for storing electronicinstructions.

As used herein, a software module or component may include any type ofcomputer instruction or computer-executable code located within a memorydevice and/or computer-readable storage medium. A software module may,for instance, comprise one or more physical or logical blocks ofcomputer instructions, which may be organized as a routine, program,object, component, data structure, etc. that perform one or more tasksor implements particular abstract data types.

In certain embodiments, a particular software module may comprisedisparate instructions stored in different locations of a memory device,which together implement the described functionality of the module. Themodule may be embodied on a computer-readable storage medium and/or as adistinct module on the storage medium. A module may comprise a singleinstruction or many instructions, and may be distributed over severaldifferent code segments, among different programs, and/or across severalmemory devices. Some embodiments may be practiced in a distributedcomputing environment where tasks are performed by a remote processingdevice linked through a communications network. In a distributedcomputing environment, software modules may be located in local and/orremote memory storage devices. In addition, data being tied or renderedtogether in a database record may be resident in the same memory device,or across several memory devices, and may be linked together in fieldsof a record in a database across a network.

It will be understood by those having skill in the art that many changesmay be made to the details of the above-described embodiments withoutdeparting from the underlying principles of the invention.

What is claimed is: 1-48. (canceled)
 49. A computer-implemented method,comprising: acquiring experimental melting curve data for a solution byuse of a melting instrument, wherein acquiring the experimental meltingcurve data comprises, melting the solution within the meltinginstrument, and measuring electro-optical (EO) radiation while meltingthe solution within the melting instrument by use of an EO sensor of themelting instrument, wherein the experimental melting curve datacomprises a background EO signal having a mathematical model; using aprocessor of a computing device to calculate function fitting parametersfor a deviation function corresponding to the experimental melting curvedata, wherein the function fitting parameters are calculated to conformthe mathematical model of the background EO signal to the experimentalmelting curve data; and using the deviation function to determinewhether the solution comprises a particular protein.
 50. The method ofclaim 49, wherein melting the solution comprises one or more of applyingenergy to the solution, heating the solution, and increasing an ionicconcentration of the solution.
 51. The method of claim 49, wherein themathematical model of the background EO radiation signal comprises aquadratic polynomial.
 52. The method of claim 49, wherein calculatingthe function fitting parameters for the deviation function comprisesselecting parameters a_(i), b_(i), and C_(i) to fit a_(i)T²+b_(i)T+C_(i)to F(T), wherein T is a melting gradient and F(T) is the experimentalmelting curve data.
 53. The method of claim 52, wherein calculating thefunction fitting parameters for the deviation function comprises fittinga_(i)T²+b_(i)T+C_(i) to F(T) using a least squares fitting technique.54. The method of claim 49, wherein calculating the function fittingparameters for the deviation function comprises fittinga_(i)T²+b_(i)T+C_(i) to the experimental melting curve data F(T) withineach of a plurality of windows within a melting gradient T, whereina_(i), b_(i), and C_(i) are fit parameters within a particular window i.55. The method of claim 54, further comprising forming the deviationfunction E(T) such that E(T)=a_(i).
 56. The method of claim 54, furthercomprising detecting protein unfolding in response to a differencebetween a_(i) and the experimental melting curve data F(T) exceeding athreshold.
 57. The method of claim 49, further comprising deriving abackground-corrected melting curve from the experimental melting curvedata and the deviation function.
 58. The method of claim 57, furthercomprising: clustering the background-corrected melting curve with aplurality of other background-corrected melting curves into two or moregroups; calculating a clustering quality metric based upon one or moreof a deviation within the two or more groups and a deviation between thetwo or more groups; and determining whether to refine thebackground-corrected melting curve based on the clustering qualitymetric.
 59. A system, comprising: a measurement instrument configured toacquire experimental melting curve data, the measurement instrumentcomprising, a vessel to hold a solution that emits electro-optical (EO)radiation in response to being melted, a melting unit to melt thesolution held in the vessel, and an EO radiation sensor configured toacquire EO radiation measurements comprising the experimental meltingcurve data; a computing device comprising a processor; an acquisitionmodule operable on the processor and configured to access theexperimental melting curve data acquired by the measurement instrument,the experimental melting curve data comprising a background EO radiationsignal having a mathematical model; and a processing module operable onthe processor configured to, construct a deviation function fromfunction fitting parameters that conform the mathematical model of thebackground EO radiation signal to the experimental melting curve data,and evaluate the deviation function to determine whether the solution isa positive sample of one of a particular nucleic acid and a particularprotein, wherein evaluating the deviation function comprises determiningwhether the deviation function indicates that the experimental meltingcurve data comprises a valid melt transition region.
 60. The system ofclaim 59, wherein the processing module is configured to segment theexperimental melting curve data into a plurality of windows, each windowcomprising a respective region of the experimental melting curve data,and to select function fitting parameters that conform the mathematicalmodel of the background EO radiation signal to the experimental meltingcurve data within each of the respective windows.
 61. The system ofclaim 60, wherein a width of the windows is selected based upon one of aresolution of the experimental melting curve data, a property of afeature of interest within the experimental melting curve data, and aperformance metric.
 62. The system of claim 59, wherein the mathematicalmodel of the background EO radiation signal comprises one of a quadraticpolynomial and an exponential decay function.
 63. The system of claim59, wherein the processing module is configured to determine thefunction fitting parameters of the deviation function by selectingparameters a_(i), b_(i), and C_(i) to fit a_(i)T²+b_(i)T+C_(i) to F(T),wherein T is a melting gradient and F(T) is the experimental meltingcurve data.
 64. The system of claim 59, wherein the processing module isconfigured to determine function fitting parameters of the deviationfunction by fitting a_(i)T²+b_(i)T+C_(i) to the experimental meltingcurve data F(T) within each of a plurality of windows within F(T),wherein a_(i), b_(i), and C_(i) are the function fitting parameterswithin a particular window i.
 65. The system of claim 64, wherein theprocessing module is configured to form the deviation function E(T_(i))such that E(T_(i))=a_(i).
 66. A non-transitory computer-readable storagemedium comprising computer-readable instructions configured to cause acomputing device to perform operations, the operations comprising:acquiring melting curve data that quantifies electro-optical (EO)radiation emitted while melting a solution within a melting instrument,wherein the acquired melting curve data comprises a background EO signalhaving a mathematical model; constructing a deviation functioncorresponding to the acquired melting curve data, wherein constructingthe deviation function comprises, calculating function fittingparameters to conform the acquired melting curve data to themathematical model of the background EO signal, and constructing thedeviation function by use of the calculated function fitting parameters;analyzing the deviation function to determine whether the acquiredmelting curve data comprises a melt transition region; and determiningwhether that the solution comprises a particular substance in responseto analyzing the deviation function.
 67. The non-transitorycomputer-readable storage medium of claim 66, wherein constructing thedeviation function further comprises: calculating respective sets offunction fitting parameters within each of a plurality of segments ofthe acquired melting curve data, wherein each set of function fittingparameters is calculated to conform the acquired melting curve data tothe mathematical model of the background EO signal within a respectivesegment; and constructing the deviation function by use of therespective sets of function fitting parameters.
 68. The non-transitorycomputer-readable storage medium of claim 66, the operations furthercomprising one of, determining that the solution comprises theparticular substance in response analyzing the deviation function toidentify a melt transition at a particular region of the acquiredmelting curve data; and determining that the solution does not comprisethe particular substance in response to analyzing the deviation functionto determine that the particular region of the acquired melting curvedata is not a melt transition, wherein the particular substance is oneof a nucleic acid and a protein.